Top AI Engineer Jobs

Design, build, and operate scalable, secure multi-cloud infrastructure and AI-focused platforms. Lead IaC automation (Terraform/Ansible/CloudFormation), enable MLOps workflows for model training and inference, apply AI-assisted engineering to reduce toil, drive platform strategy, deliver large-scale projects, and mentor engineers.

Design and implement scalable platform services, runtimes, and developer tooling for deploying and managing AI workloads on Arm-based edge devices. Build abstractions, observability, and performance optimizations across heterogeneous hardware while collaborating with architects and AI engineers to produce robust, maintainable systems and SDKs for on-device AI.

Build and scale backend and full-stack systems powering patient booking, clinician routing, logistics, and device integrations. Own 0->1 projects, design APIs and data models, solve scheduling and dispatch challenges, integrate external health systems, and collaborate with product, data, ops, and clinical teams.

Build and scale backend and full‑stack systems powering patient booking, clinician routing, logistics, and device integrations. Ship end-to-end features, design APIs and data models, solve scheduling/dispatch challenges, integrate with external health systems and medical devices, and own projects from 0→1 while collaborating with product, data, ops, and clinical teams.

As an AI Forward Deployed Engineer, you'll manage customer onboarding by integrating AI Agents, architecting solutions, and ensuring project success through strategic problem-solving and communication.

Lead system safety efforts for autonomous and AI-enabled systems, analyzing hazards, requirements, safety architectures, mitigations, verification strategies, and digital safety cases. Apply Guardian AI to safety analyses, evaluate AI-generated outputs, establish validation practices, and convert recurring customer problems into product capabilities. Advise customers, mentor engineers, contribute to standards and industry thought leadership, and help define AI-assisted safety engineering methods.

Build Town’s greenfield AI context and retrieval infrastructure, including hybrid lexical and semantic search, durable data models, realtime and batch pipelines, indexing, and storage. Optimize latency, cost, freshness, completeness, and reliability at scale. Establish the foundation for a knowledge graph connecting people, companies, projects, and content, while taking systems from prototype through production.

Own evaluation, selection, and continuous optimization of LLMs powering the analytics platform. Build Eval frameworks, benchmark and migrate models, implement RAG and embedding-based pipelines, optimize cost/performance, deploy model changes to cloud production, monitor performance and drift, and document methodologies and runbooks. Collaborate with product and operations and take end-to-end ownership of AI engineering work.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines for legal and medical document tasks. Build Eval frameworks, implement RAG and embedding-based systems, integrate and transition models into production on cloud, monitor performance and cost, and document runbooks. Collaborate cross-functionally and take end-to-end ownership of deployments and validation.

Own evaluation, selection, and continuous optimization of LLMs and AI processes powering the analytics platform. Build EvalOps frameworks, benchmark and transition models, implement RAG/embedding/vector DB solutions, optimize orchestration pipelines, deploy model changes end-to-end, monitor performance/cost/drift, and document runbooks. Collaborate cross-functionally and execute production deployments.

Own evaluation, selection, and production optimization of LLMs and AI pipelines for legal and medical document tasks. Design Eval frameworks, benchmark models for accuracy, cost, and speed, implement model transitions, build and deploy RAG and embedding-based solutions on cloud, monitor production performance, and maintain documentation and runbooks with end-to-end ownership.

Own evaluation, selection, and production integration of LLMs. Build EvalOps frameworks, benchmark models, optimize RAG and orchestration pipelines, deploy model changes to cloud, implement monitoring, and document runbooks while collaborating with product and operations.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines. Build EvalOps frameworks, benchmark models, implement model transitions, optimize RAG/document understanding pipelines, deploy and validate production changes, monitor performance/cost, and document runbooks.

Serve as a technical product engineer advising customers on integrating Claude and LLMs into production products. Lead discovery, prototypes, eval frameworks, workshops, code reviews, and deployments while contributing patterns and documentation to product and engineering teams. Travel occasionally for customer support and represent Anthropic at events.

Design, build, and operate distributed GPU training infrastructure for large neural networks. Optimize multi-node/multi-GPU execution, diagnose compute/memory/network bottlenecks, improve stability and fault tolerance, and partner with research teams to productionize large-model training pipelines for reliable, high-throughput training at scale.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

As a Staff/Principal AI Engineer, you'll design and implement AI strategies for legal tech, lead engineering teams, and guide AI projects while collaborating across departments.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead product strategy and delivery for Capital Ones AI coding and developer experience tools. Translate frontier AI into governed, developer-loved products that scale AI-augmented workflows, improve code review, embed enterprise standards, and accelerate software delivery through cross-functional leadership and data-driven decisions.

Own and sustain an enterprise AI assistant supporting engineering, manufacturing, quality, and supply chain. Responsibilities include platform maintenance, dependency and model updates, regression testing, data-drift remediation, guardrails, feedback triage, performance reporting, new AI workflow development, model evaluation, technical leadership, mentoring, rollout support, and AI strategy planning.

Develop and deploy machine learning solutions for construction-site understanding using computer vision, multimodal AI, VLMs, LLMs, generative AI, RAG, and agent architectures. Curate high-quality datasets, optimize models for performance across hardware targets, and build production ML pipelines in the cloud. Own components from design through deployment and monitoring while collaborating in an agile team.

Design and optimize LLM prompts, collaborate with domain experts to translate business needs into prompt-driven solutions, integrate LLMs into applications, build a reusable prompt engineering framework, gather user feedback, document best practices, and stay current on LLM tools and platforms.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

The AI Engineer will prototype and implement innovative document search methods, owning impactful projects as part of Humata's cognitive architecture team.

As a Fullstack AI Engineer, you'll prototype frameworks and scale real-time engines, focusing on product feel without handed specs.