Ai Engineering Jobs with Salary ≥ 100k

Summary: Candidates must possess 3 or more years of experience in software or ML engineering post-BS/MS, with hands-on experience shipping production AI/ML systems or AI-powered products. Strong skills in building multi-step ML systems, backend/API development, and demonstrating startup grit in fast-moving environments are required.

Skills: ai agent development, model development, orchestration, productionization, evaluation, platform engineering, backend development, frontend development, api development, data pipelines, system architecture, inference optimization, scalability, reliability engineering, ml systems shipping

Summary: Join a small, mission-driven research startup with a culture of ownership and collaboration; this role blends deep science with production-readiness and product impact; visa sponsorship available; strong growth and learning opportunities.

Skills: python, pytorch, llms, ai_infrastructure

Summary: Candidates should possess a solid level of experience with JavaScript and TypeScript, particularly in building dynamic web applications, and be comfortable working with React alongside backend systems. Successful candidates must be proactive problem-solvers who thrive in fast-paced environments, deliver high-quality products, and are eager to mentor others.

Skills: javascript, typescript, react, frontend, backend, real-time infrastructure, ai, debugging, optimization, testing, observability, ux

Summary: Candidates must have at least five years of experience applying AI/ML research to ship user-facing, production-grade products, possessing a strong software engineering background. Required expertise includes fine-tuning and deploying large language models, proficiency in prompt engineering, and comfort building scalable data-driven feedback loops.

Skills: predictive ai features, natural language detection, autosuggestions, intelligent prompt recommendations, code generation quality, fine-tuning llms, prompt engineering, data-driven feedback loops, context retrieval systems, software engineering, agent development, product implementation

Summary: Candidates must have successfully taken AI-powered systems from initial idea through production and at least one iteration cycle with real users, possessing strong engineering fundamentals for end-to-end ownership of services and data flows. Required experience includes working with LLM APIs, RAG architectures, and structured tool-calling, coupled with a bias toward action and comfort with ambiguity.

Skills: ai systems design, production deployment, engineering fundamentals, llm apis, embeddings, retrieval augmented generation, tool calling, agents, api stitching, webhooks, event systems, observability, workflow automation, experimentation, data flows, integrations

Summary: Candidates must have over 5 years of experience building and deploying production AI/ML systems, including hands-on operationalization of LLM-based systems and experience with multimodal or vision systems. Strong proficiency in Python and experience with PyTorch or TensorFlow, along with distributed/cloud infrastructure for ML workloads, is required.

Skills: multimodal ai, llms, asr models, vision architectures, rag pipelines, prompt-based approaches, tool-using agents, inference systems, pytorch, tensorflow, ml lifecycle, gpu optimization, mlops, latency optimization, cost efficiency, safety guardrails

Summary: Candidates must possess strong Python skills with over 5 years of experience building production backend or data engineering systems, alongside hands-on experience with LLM architectures like RAG and prompt engineering. Desired qualifications include fluency in Azure infrastructure, experience with search/retrieval systems, and familiarity with the MS365 ecosystem or workflow automation platforms.

Skills: python, llm apis, rag architectures, prompt engineering, evaluation frameworks, full-stack development, azure, semantic search, workflow automation, voice ai, node.js, typescript, data pipelines, api integrations, orchestration, llm agents

Summary: Candidates must possess deep hands-on software engineering expertise and platform-level thinking to build scalable, distributed ML systems, coupled with mastery of MLOps practices and cloud proficiency (GCP/AWS). Expert-level Python is required, along with familiarity with modern ML frameworks and orchestration tools like LangChain or LLamaIndex.

Skills: ai systems design, ml productionization, distributed software engineering, large language models, rag pipelines, agentic loops, evaluation frameworks, observability, python, mlops, kubernetes, docker, gcp, aws, langchain, llamaindex

Summary: Candidates must possess over 15 years in relevant engineering fields, including at least 5 years in senior/principal leadership roles and 3 years leading teams, with significant experience in client-facing environments. Essential technical expertise includes deep proficiency in agentic AI frameworks like LangChain, hands-on building of autonomous agent capabilities, and strong skills in Python, Spark, RAG pipelines, and LLMOps practices.

Skills: agentic ai, platform architecture, distributed systems, llmops, mlops, aiops, python, spark, langchain, rag, graphdb, vector databases, data ingestion, system scalability, ai governance, team leadership

Summary: Candidates must have 10+ years of software development experience, including time as an Individual Contributor and a mentor/lead, with hands-on experience building production applications using LLMs technologies like RAG or fine-tuning. A strong background in web development, engineering leadership, and proficiency with various database types (SQL/NoSQL) and TypeScript is required.

Skills: software development, llms, prompt engineering, rag, fine-tuning, agentic workflows, sql, nosql, postgres, cassandra, elasticsearch, graph databases, typescript, backend web development, rest apis, cloud environments

Summary: Candidates must have experience with various programming languages like TypeScript, Python, and Go, alongside strong skills in cloud platforms such as Google Cloud, AWS, or Azure. Success requires technical decision-making ability, project leadership, team mentoring, and a background in building scalable systems with both front-end and back-end technologies.

Skills: full stack engineering, ai integration, system design, technical leadership, next.js, react, angular, fastapi, django, spring boot, .net, postgresql, bigquery, mongodb, redis, kafka

Summary: Candidates must have significant hands-on experience with Supervised Fine-Tuning (SFT) of LLMs in production environments, moving beyond prompt-only approaches, and direct experience using OpenAI APIs or AWS Bedrock for post-training tasks. Strong proficiency in Python, ML frameworks like PyTorch, and experience operating ML systems in distributed production environments are required.

Skills: supervised fine-tuning (sft), large language models (llms), preference optimization, agentic llm systems, instruction tuning, rlhf, direct preference optimization, model evaluation, a/b testing, prompting, retrieval-augmented generation (rag), tool use, python, pytorch, aws bedrock, openai apis

Summary: Candidates must possess a minimum of 10 years of progressive leadership experience in areas like digital health or product development, including 10+ years in software engineering with at least 5+ years in AI/ML leadership roles. A Bachelor’s degree in a related field is required, with an advanced degree preferred, alongside deep technical expertise in ML algorithms, deep learning, NLP, and cloud AI services.

Skills: artificial intelligence, machine learning, ai strategy, model building, ethical ai, agile methodologies, user-centricity, design systems, cybersecurity governance, team leadership, python, tensorflow, pytorch, cloud ai services, generative ai, model interpretability

Summary: Candidates must have a minimum of 10 years of progressive leadership experience in digital health, product development, or related fields, including at least 5+ years in AI/ML leadership roles with a proven track record of delivering AI-driven products at scale. A Bachelor’s degree in a related field is required, with an advanced degree preferred.

Skills: artificial intelligence, machine learning, ai strategy, scalable ai models, ethical ai, agile methodologies, user-centricity, design systems, information security, team leadership, python, tensorflow, pytorch, scikit-learn, generative ai, llms

Summary: Candidates must be strong conceptual thinkers capable of structuring complex business challenges into data problems, with a passion for LLMs and real-world AI applications. Required experience includes a PhD with 3+ years of industry experience or a Bachelor's/Master's with 7+ years, plus at least 1 year of team management and hands-on experience building AI applications.

Skills: large language models, data science, team leadership, ontology transformation, actionable insights, agentic applications, project management, model development, production-ready systems, system architecture, prompt engineering, advanced analytics, unstructured data transformation, mentoring

Summary: Candidates must possess a minimum of 2 years of software engineering experience along with 2+ years working hands-on with AI/ML systems, including experience with LLMs and/or VLMs. Essential technical skills include strong Python programming, familiarity with AI/ML frameworks like PyTorch or TensorFlow, and proficiency with foundation model APIs and prompt engineering.

Skills: large language models, vision language models, speech recognition, conversational ai, document understanding, ocr, human-in-the-loop, multimodal ai, model fine-tuning, prompt engineering, python, pytorch, tensorflow, foundation model apis, cloud-based ai infrastructure, model deployment

Summary: Candidates must possess over 7 years of hands-on engineering experience, with a strong requirement for leading greenfield or 0-to-1 product builds involving AI systems or data-intensive architectures. Proficiency in Python and experience integrating LLMs, vector databases, and cloud services are essential for this role.

Skills: greenfield development, zero to one products, ai systems, automation, workflow engines, data intensive architectures, cloud deployment, aws, system design, python, llms, embeddings, prompt orchestration, model evaluation, ci/cd, observability

Summary: Candidates must have at least five years of experience applying AI/ML research to ship user-facing, production-grade products, possessing a strong software engineering background. Proficiency in fine-tuning and deploying large language models, adeptness at prompt engineering, and comfort building scalable data-driven feedback loops are essential.

Skills: predictive ai features, natural language detection, autosuggestions, intelligent prompt recommendations, code generation quality, fine-tuning, prompt engineering, data-driven feedback loops, context retrieval systems, software engineering, large language models, agent development, production deployment

Summary: Candidates must have over 5 years of production Python experience at scale and strong AWS expertise, along with familiarity with agentic AI architectures like LangChain or AutoGen. The role requires comfort working across the stack, including shipping small UI features using React/Next.js and familiarity with relational databases.

Skills: python, aws, lambda, s3, cloudwatch, llms, agentic ai architectures, langgraph, langchain, autogen, llamaindex, infrastructure as code, cdk, terraform, ci/cd, typescript

Summary: Candidates must possess over 5 years of production Python experience at scale, strong AWS expertise across several services, and familiarity with relational databases. Required experience also includes agentic AI architectures, data pipeline development (streaming/batch), Infrastructure as Code, and production SaaS experience including on-call participation.

Skills: python, aws, lambda, dynamodb, s3, glue, athena, mysql, postgresql, oracle, agentic ai architectures, data pipeline development, infrastructure as code, cdk, terraform, github actions

Summary: Candidates must have 2+ years of software engineering experience and demonstrate an ownership mentality for AI systems, taking responsibility for their production value. A key requirement is experience building end-to-end AI systems powered by LLMs, not just calling APIs, alongside strong engineering fundamentals in clean, maintainable Python.

Skills: software engineering, ai systems operation, system design, llm applications, agent design, retrieval, evaluation frameworks, python, debugging, testing, system integration, stakeholder communication, prompt engineering, ai tool usage

Summary: Candidates must have at least two years of experience in both engineering management and Computer Vision, including a demonstrated track record of defining and implementing roadmaps that incorporate experimental R&D. Firsthand experience training Computer Vision models and experience at early-stage startups are also required.

Skills: computer vision, engineering management, strategy, roadmap definition, team leadership, hiring, onboarding, performance management, cross-functional collaboration, technical leadership, ai, machine learning, r&d, model training

Summary: Candidates should have 8+ years of experience in software engineering or applied machine learning, focusing on building real-world AI/ML systems, or equivalent experience in healthcare-specific AI solutions. Proficiency in Python backend engineering, solid understanding of embeddings/embedding databases, and experience deploying cloud-native applications on AWS are required.

Skills: artificial intelligence, machine learning, python, language technologies, voice technologies, agentic ai, ml/ai architecture, sdlc ownership, backend services, technical leadership, aws bedrock, nlp, embeddings, embedding databases, cloud-native applications, technical mentorship

Summary: Candidates must have at least two years of experience in software engineering and/or machine learning applied in production, including hands-on experience with LLM applications, prompt engineering, and evaluation frameworks. Required technical expertise includes working with vector or graph databases, document chunking, embedding models, and familiarity with metadata-based retrieval and re-ranking strategies.

Skills: llm, rag systems, vector search, hybrid retrieval, reranking, contextual compression, agent systems, information extraction, information storage, information retrieval, mlops, prompt engineering, model evaluation, graph databases, embedding models, ci/cd

Summary: Candidates need 12+ years in cloud software development with 5+ years specializing in AI/ML, requiring expertise in generative AI models, deep learning frameworks (TensorFlow, PyTorch, JAX), and programming languages like Python. A Bachelor's, Master's, or Ph.D. in a related technical field is required, along with strong skills in MLOps and system design.

Skills: generative ai, deep learning, tensorflow, pytorch, jax, python, transformer architectures, model training, fine-tuning, prompt engineering, ml ops, docker, kubernetes, data engineering, system design, ci/cd

Summary: Candidates must possess a Bachelor's degree in a relevant technical field and have at least 5 years of experience in SRE, DevOps, or Systems Engineering, including 2 years applying AI/ML techniques in operational contexts. A mandatory requirement for consideration is holding an active Secret Clearance.

Skills: site reliability engineering, automation, orchestration, artificial intelligence, machine learning, aiops, observability, anomaly detection, incident management, root-cause analysis, automation playbooks, infrastructure-as-code, knowledge management, predictive reliability engineering, data analysis, distributed systems

Summary: Candidates should be engineers who prioritize evaluation as foundational for reliable AI systems, possessing an evaluation-first mindset and data-driven rigor. Required experience includes shipping production software in complex systems, building and deploying LLM-powered features, and implementing evaluation frameworks, with familiarity in TypeScript, Python, and Postgres.

Skills: evaluation infrastructure, unified platform, automated pipelines, production feedback loops, ml engineering, observability, quality assurance, llms, langsmith, langgraph, retrieval systems, model evaluation, guardrails, typescript, python, postgres

Summary: Candidates must have 5+ years of experience in AI/ML engineering, strong Python and backend skills, and a solid foundation in statistics and ML theory. Proven hands-on experience building with LLMs (including RAG, fine-tuning, etc.) and a track record of taking ML models from prototype to production are essential.

Skills: python, backend engineering, statistics, ml theory, llms, prompt engineering, rag, embeddings, fine-tuning, agent orchestration, voice ai, asr, tts, conversational ai, graphql, kubernetes

Summary: Candidates must possess strong product management experience owning developer-facing products, coupled with a strong technical foundation including hands-on software development and comfort using the command line daily. Familiarity with the AI coding assistant landscape and experience defining product strategy for CLI-first experiences are also essential.

Skills: product strategy, roadmap definition, cli experience, ide extensions, ai developer tools, devsecops platform, gitlab duo, command line workflows, product execution, developer experience, product quality, iterative delivery, competitive analysis, software development, b2b saas, developer platforms

Summary: Candidates should have proficiency in Python or Typescript and experience with LLM tooling. A deep understanding of agent design patterns and responsible AI practices is also required.

Skills: python, typescript, llm tooling, langchain, llamaindex, guardrails, mcp, agents sdk, agent design patterns, observability, debugging, human-in-the-loop feedback systems, cloud environments, devops tools, architectural design, systems design

Summary: Candidates should have deep technical expertise in machine learning and a passion for developing novel systems. They should thrive in research-oriented environments and care about measurable impact in students' learning outcomes.

Skills: machine learning, ai systems, research, data analysis, collaboration, experimentation, human-centered design, engineering solutions

Summary: Candidates should have 5+ years of AI modeling experience in the self-driving car industry or a PhD with 3+ years of relevant experience. A proven track record in developing and deploying large-scale ML models and familiarity with modern ML architectures is essential.

Skills: machine learning, ai modeling, robotics, diffusion models, autoregressive models, reinforcement learning, model debugging, performance optimization, python, pytorch, docker, nodejs, typescript, model evaluation, data strategies, training pipelines

Summary: Candidates need extensive technical aptitude, practical experience in managing technical systems, and proven leadership skills to mentor teams and manage complex projects through the full SDLC. A Bachelor's degree in Computer Science plus twelve years of engineering experience, including five years in leadership, is required.

Skills: ai engineering, technology leadership, application development, infrastructure management, security, network management, developer experience, cross-functional collaboration, platform capabilities, software engineering organization, data utilization, automation, roi analysis, saas technologies, cloud architecture, operational compliance

Summary: Candidates must have over 5 years of software engineering experience, exposure to AI/ML technologies, and strong skills in both frontend development using TypeScript/JavaScript and React, and backend development using TypeScript or Python focusing on API design. A high degree of ownership and the ability to drive features independently are essential.

Skills: typescript, javascript, react, python, api design, service architecture, ai/ml, inference api, frontend, backend, monitoring, versioning, observability, evaluation, data-oriented interfaces, vector databases

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates must have a degree in a relevant technical field and significant professional experience, including at least one year in Generative AI technologies, or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates must have a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies, or relevant certifications. Essential qualifications include strong proficiency in Python and SQL, experience shipping and operating LLM-enabled features in production, and strong analytical and communication skills.

Skills: generative ai, agentic workflows, llms, tensorflow, pytorch, python, r, sql, data processing, algorithm development, machine learning models, experiment design, telemetry, observability, data engineering, problem solving

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least one year in Generative AI technologies or relevant certifications. Essential requirements include strong problem-solving skills, proficiency in Python and SQL, experience shipping and operating LLM-enabled features, and familiarity with modern data/analytics platforms.

Skills: generative ai, agentic workflows, llms, telemetry, observability, data ingestion, data transformation, retrieval strategies, prompt engineering, model evaluation, code reviews, architecture discussions, incident response, data privacy, responsible ai, python

Summary: Candidates need a degree in a relevant field and significant professional experience, including at least 1 year in Generative AI technologies, or relevant certifications. Essential requirements include strong Python and SQL skills, experience shipping and operating LLM-enabled features, data-intensive system building, and strong problem-solving abilities.

Skills: generative ai, agentic workflows, llms, tensorflow, pytorch, python, r, sql, data processing workflows, machine learning models, algorithm development, data ingestion, data transformation, retrieval strategies, experiment design, code reviews

Summary: YC-funded startup with meaningful ownership; remote-first and high-growth environment; chance to shape foundational AI/ML systems in benefits at scale.

Skills: python, node.js, openai, anthropic, google ai studio, langchain, langgraph, llamaindex, aws, mysql, pinecone, mlflow, langsmith, langfuse

Summary: Candidates need over 5 years of experience in Conversational AI or IVR development, with strong Java skills including VXML and backend integration experience being essential. Familiarity with contact center platforms like Genesys and tools such as JIRA is also required.

Skills: conversational ai, ivr development, java, vxml, backend integration, genesys, jira, cyara, confluence, rest apis, microservices, aws, sql, nosql, kafka, git

Summary: Candidates must have 3 to 7+ years of experience in Revenue Operations, GTM Systems, or similar roles focused on improving GTM execution through systems and automation, demonstrating a pattern of solving ambiguous problems quickly. Required technical comfort includes APIs, webhooks, JSON payloads, SQL, and strong Salesforce fluency.

Skills: ai agents, automation, data flow, relevance ai, momentum, clay, llm tooling, gtm ai strategy, salesforce fluency, apis, webhooks, json payloads, sql, revenue operations, gtm systems, operational rigor

Summary: Candidates typically need 8+ years in Software/ML Engineering/Data Science, with at least 3 years hands-on in Applied AI/LLMs and 2+ years in a senior/lead capacity, requiring deep expertise in Python/TypeScript and distributed cloud systems. Proven success in architecting and shipping complex AI systems at scale, along with strong stakeholder management skills, is essential.

Skills: generative ai, agentic ai, strategy, architecture, foundation models, rag, multi-agent systems, llms, python, typescript, javascript, cloud-native systems, kubernetes, ci/cd, prompt design, model evaluation

Summary: Candidates must have 7+ years of professional frontend development experience, deep expertise in React JS and TypeScript, and strong experience building data-dense or visualization-heavy web applications, including map-based UIs with layered GIS data. Experience with Canvas, Web Workers, or WebGL-based rendering techniques is required.

Skills: frontend development, geospatial data, computer vision, react js, typescript, data visualization, gis, map-based uis, canvas, web workers, webgl, performance optimization, api integration, product minded, saas applications, ai-assisted development

Summary: Candidates must have at least two years of experience in software engineering and/or applying machine learning in production, including hands-on experience with LLM applications, prompt engineering, and evaluation frameworks. Required technical expertise includes familiarity with vector or graph databases, document chunking, embedding models, and model evaluation metrics.

Skills: llm, rag systems, vector search, hybrid retrieval, reranking, contextual compression, agent systems, information extraction, information storage, information retrieval, mlops, prompt engineering, model evaluation, graph databases, embedding models, ci/cd

Summary: Candidates must possess an MS degree in Computer Science, AI, Machine Learning, or a related field, along with 3+ years of experience in AI and software development in a commercial setting. Required technical skills include proficiency in languages like Python and experience with frameworks such as PyTorch and TensorFlow.

Skills: ai solutions engineering, machine learning implementation, generative ai, statistical techniques, python, java, r, pytorch, tensorflow, cloud platforms, aws, google cloud, devops tooling, technical communication, deep learning, natural language processing

Summary: Candidates must be authorized to work in the US and possess a minimum of 3 years of experience in AI/ML development, data science, or software engineering, with proficiency in Python or Java. Familiarity with data engineering tools like Hadoop/Spark, cloud services (AWS/Azure), machine learning frameworks (TensorFlow/PyTorch), and SQL is required.

Skills: ai development, machine learning, data collection, data analysis, etl pipelines, optimization, automation, model testing, validation, collaboration, python, java, tensorflow, pytorch, sql, problem-solving

Summary: Candidates need a degree in a relevant field and significant professional experience (8+ years with Bachelor's or 6+ years with Graduate degree), including at least 1 year in Generative AI technologies. Essential qualifications include strong problem-solving skills, experience shipping and operating LLM-enabled features in production, and proficiency in Python and SQL.

Skills: generative ai, large language models, llms, ai workflows, architecture, deployment, monitoring, python, sql, data engineering, apis, data transformation, prompt engineering, evaluation, observability, typescript

Summary: Candidates must possess strong software engineering skills with a proven track record of delivering production software systems, coupled with practical AI experience focusing on real-world solutions. Essential requirements include experience with LLMs, prompt engineering, building GenAI applications, and exposure to cloud-native environments and observability tools.

Skills: ai solutions development, rapid experimentation, cross-functional collaboration, llms, prompt engineering, genai applications, production software systems, cloud-native environments, observability tools, agent frameworks, infrastructure tooling, model fine-tuning, software engineering, incident response, automation

Summary: Candidates must possess strong software engineering skills with experience building production backend or full-stack systems, coupled with practical AI experience focused on real-world solutions. Essential requirements include experience with LLMs, prompt engineering, GenAI applications, production delivery track record, cloud-native exposure, and familiarity with observability tools.

Skills: software engineering, ai, ml, llms, prompt engineering, genai, backend development, full stack development, experimentation, cloud-native environments, observability tools, agent frameworks, kubernetes, docker, terraform, model fine-tuning

Summary: Candidates should have a Bachelor’s degree in Computer Science or a related field with 2+ years of experience, or a Master’s degree with relevant experience. Proficiency in programming languages such as TypeScript/JavaScript and Python, along with experience in building full-stack applications, is required.

Skills: software engineering, ai integration, full-stack development, frontend development, backend services, api integration, generative ai, large language models, agent frameworks, testing, debugging, monitoring, collaboration, problem solving, ethics, security

Summary: A Master’s degree in a quantitative field and at least 4 years of experience in machine learning and statistics are required. Candidates should also be comfortable using Linux and have the ability to obtain a Top Secret clearance.

Skills: machine learning, statistics, data science, signal processing, cloud infrastructure, python, sql, data pipelines, etl processes, model deployment, linux, ml governance, tensorflow, pytorch, scikit-learn, experiment tracking, reproducible research

Summary: Candidates must have 7+ years of backend, data, or infrastructure engineering experience, demonstrating expertise in distributed systems, big data infrastructure, and applied AI/ML, including LLMs. Proficiency in languages like Java, Go, or Python, along with strong SQL skills and familiarity with tools like Kafka and ClickHouse, is required.

Skills: distributed systems engineering, high-throughput apis, scalable microservices, event processing pipelines, big data infrastructure, applied ai/ml, large language models (llms), java, go, python, sql, data modeling, kafka, kinesis, clickhouse, llm-powered systems

Summary: A Master’s degree in a quantitative field and at least 4 years of experience are required, along with a strong background in machine learning, mathematics, and statistics. Candidates must be comfortable using Linux and have the ability to obtain a Top Secret clearance.

Skills: machine learning, statistics, data science, signal processing, cloud computing, python, sql, data engineering, etl processes, model deployment, linux, governance practices, experiment tracking, model monitoring, reproducible research, information theory, adversarial ai

Summary: The ideal candidate is a senior full-stack engineer comfortable across the entire stack who possesses genuine curiosity about AI and is excited to apply it creatively to real-world problems. They must be product-minded, self-directed, thrive with autonomy, and be a clear communicator within a fast-moving team.

Skills: full stack engineering, ai integration, backend services, frontend interfaces, autonomous ai systems, platform scaling, react, node.js, postgres, aws, product ownership, technical direction, user feedback iteration, vercel, inngest, twilio/whatsapp

Summary: Candidates should have proven experience in building and operating production AI systems and implementing the full lifecycle of early-phase AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a strong understanding of connecting modeling tasks with business value, is essential.

Skills: ai systems, machine learning, deep learning, data analysis, prototyping, model deployment, healthcare data, generative ai, ranking, categorization, search, collaboration, mentorship, software engineering, business objectives, performance measurement

Summary: Candidates should have proven experience in building and operating production AI systems and implementing the full lifecycle of AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a strong understanding of business value in AI, is essential.

Skills: ai development, outcome-driven engineering, ai system design, mentorship, data science, machine learning, deep learning, pytorch, tensorflow, generative ai, aws, azure ml, databricks, healthcare, model evaluation, collaboration

Summary: Candidates should have proven experience building and operating production AI systems and implementing the full lifecycle of AI development. Familiarity with tools like PyTorch and TensorFlow, as well as a relevant advanced degree, is advantageous.

Skills: ai systems, unstructured data, model implementation, ai performance, collaboration, prototyping, deep learning, pytorch, tensorflow, hugging face, databricks, aws bedrock, azure ml

Summary: Candidates should have experience with AI/ML systems in production, familiarity with prototyping AI solutions, and an understanding of the AI development lifecycle. Hands-on experience with tools like PyTorch and TensorFlow is preferred, along with the ability to connect technical work with business value.

Skills: ai, machine learning, healthcare, data management, model implementation, collaboration, pytorch, tensorflow, generative ai, monitoring, evaluation, stakeholder engagement, prototyping, deployment, business value, technical expertise

Summary: Candidates should have solid professional experience in software engineering, ideally 5+ years, with strong coding skills in C#, JavaScript, TypeScript, React, and Node. Familiarity with AI application patterns and tools, as well as cloud services, is also required.

Skills: c#, javascript, typescript, react, node, ai tools, llms, rag pipelines, vector search, ai-driven analytics, problem-solving, communication, collaboration, cloud services, ai code generation, mentorship

Summary: Candidates should have a degree in Computer Science, AI, Machine Learning, or a related field, along with 5+ years of experience in developing AI/ML solutions. Expertise in Python and agent frameworks is essential, along with strong collaboration and mentorship skills.

Skills: python, ai, machine learning, autonomous agents, agent frameworks, generative ai models, fastapi, asyncio, pytorch, distributed systems, cloud-native architectures, collaboration, mentorship, communication