Top AI Engineer Jobs

Lead design, build, test, and deploy AI agents and workflow automations; map and redesign business processes to integrate AI; support client engagements across discovery, deployment, and adoption; train nontechnical users and produce governance, documentation, and reusable solution assets.

Design and operate compliant, audit-ready data-quality and active-learning pipelines for medical imaging and metadata on cloud infrastructure. Build dataset versioning, de-identification, validation gates, lineage, and active-learning loops to prioritize labeling and measure label quality. Collaborate with AI engineers, clinicians, data stewards, and external annotation teams to deliver datasets fit for regulated medical-device software development.

Lead design, development, and production deployment of autonomous AI agents and multi-agent systems. Build RAG pipelines, vector DB integrations, agent orchestration, observability and guardrails, integrate agents with enterprise systems, and drive AI architecture and best practices across teams to enable intelligent workflow automation and decision-making.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Build and maintain Premonition, an internal platform for managing, deploying, and monitoring agentic LLM pipelines. Develop composable agents, tools, APIs, orchestration, and infrastructure on AWS/EKS. Optimize high-performance data layers (Iceberg/Delta/Hudi), embedded engines (DuckDB, LanceDB), and ensure observability, safety, and secure integration with SOC, IR, and threat-hunting workflows.

Design, build, deploy, and maintain production-grade AI/ML applications (including LLMs and agentic systems). Implement APIs, services, data pipelines, testing, monitoring, and CI/CD. Collaborate with data scientists, platform, security, and product teams to deliver secure, scalable, observable AI solutions that create measurable business value.

As a Senior Backend Engineer at Posh, you will design and scale backend services, manage AI infrastructure, and collaborate on integrating AI features into products.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

As a Senior Software Engineer II, you'll design and operate a Kubernetes-native platform for AI workload orchestration, improve performance, and mentor engineers.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, develop, and deploy AI-native software that converts large-scale geospatial data into actionable intelligence. Build multi-agent workflows and integrate LLMs (GPT-4, Claude, Gemini) for contextual analysis and autonomous reasoning. Engineer agents with tool use, iterative self-refinement, and feedback loops; develop data ingestion and transformation layers; ensure secure, scalable cloud integrations; and deploy/operate systems in production using containerization, orchestration, and CI/CD.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Develop, deploy, and operate agentic AI systems that drive manufacturing decisions across S&OP, material planning, production, quality, logistics, and customer deployments. Embed with cross-functional teams to model demand, supply, BOMs, capacity, and risk; build agents that generate executable plans, propagate design changes, run historical replays and live evals, and continuously evaluate frontier models and optimization methods to improve ramp speed and schedule attainment.

Founding Full-Stack AI Engineer to architect, build, and scale an enterprise digital account manager. Own end-to-end features, build integrations and data ontology mapping, develop intuitive UIs, and design agentic LLM solutions (memory, RL). Work directly with founders and customers to iterate rapidly and deliver enterprise-grade, scalable AI products onsite in San Francisco.

Lead and mentor multiple engineering teams to deliver AI-enabled MSK platform solutions. Drive AI strategy, design and deploy responsible ML solutions, ensure regulatory (CMS) compliance, oversee vendor partners, modernize platform architecture, and manage production support and incident governance.

Build, optimize, and deploy production-grade generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and host LLMs, integrate/swap commercial and open-source models, and deliver scalable FastAPI/async backends with Docker, monitoring, and CI/CD.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Design, implement, and optimize embodied AI model architectures and deploy them at scale. Build training/evaluation infrastructure and large-scale data pipelines combining simulation, teleoperation, generative and real-world data. Work hands-on with robot hardware (manipulators, mobile robots, RGB-D and tactile/FT sensors) to debug failures and close the sim-to-real loop. Collaborate with global research and hardware teams to integrate systems into consumer products.