Top AI Engineer Jobs

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Design, build, deploy, and maintain AI-enabled applications and automation tools that improve engineering workflows. Develop LLM assistants, AI agents, document intelligence, developer productivity utilities, and enterprise integrations. Establish evaluation, monitoring, security, privacy, access-control, and responsible AI practices. Collaborate with engineering, security, IT, governance, and business teams to move AI prototypes into reliable production solutions.

Deploy and operate production generative AI and agentic workloads for enterprise customers. Design large-scale LLM and multimodal inference architectures, manage Kubernetes-based deployments, troubleshoot performance issues, integrate infrastructure into CI/CD workflows, and develop reusable Helm, Terraform, and automation solutions. Collaborate with customer engineering teams, contribute to platform reliability and observability strategies, and lead technical workshops on AI infrastructure best practices.

Build production AI and agentic workflows combining physics-informed models, LLMs, VLMs, scientific data, retrieval, memory, tools, and deterministic rules. Develop secure, durable systems with permissions, auditability, human approval, evidence lineage, and recovery capabilities. Evaluate system performance for task success, groundedness, safety, latency, cost, and scientific validity. Partner with product and design teams to deliver customer-facing intelligence products supporting operational decisions.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Leads backend architecture and technical strategy for autonomous vehicle and customer support systems. Designs scalable integrations across Fleet, Uber for Business, Direct, and platform teams; develops proactive trip-lifecycle support, Agentic AI capabilities, reliability, and end-to-end testing frameworks. Drives cross-functional technical alignment and mentors senior engineers toward Staff-level impact.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Designs and leads scalable AI-ready data solutions for pharmaceutical value-chain use cases. Responsibilities include building data pipelines and models, integrating LLMs, operationalizing machine learning models, developing graph and semantic data capabilities, researching AI platforms, and advising stakeholders on emerging technologies. The role requires technical leadership, collaboration across engineering and business teams, and adherence to software development, governance, quality, and security standards.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, resource utilization, training pipelines, developer tooling, and operational processes. The role partners with platform, orchestration, performance, and machine learning teams to diagnose training issues and support production-ready AI workloads.

Build and scale distributed training infrastructure for large AI models across GPU clusters. Improve training reliability, efficiency, fault tolerance, checkpointing, and resource utilization. Integrate training systems with production ML pipelines, develop tooling and automation to improve developer experience, diagnose training throughput and stability issues, and establish operational best practices for a growing AI infrastructure platform.

Build and scale distributed infrastructure for large-scale AI model training across GPU clusters. Responsibilities include improving reliability, fault tolerance, checkpointing, recovery, throughput, resource utilization, and cost efficiency. The role integrates models into production training pipelines, develops automation for AI researchers, diagnoses distributed training issues, and establishes platform reliability practices. Candidates should have experience with distributed training systems, foundation models, multi-node GPU workloads, complex distributed systems, and ML infrastructure at scale.

Build and operate production AI agents that automate accounting workflows and help customers understand financial data. Shape agent architecture, develop customer-facing features, establish reliability practices, and own evaluations, tracing, monitoring, permissions, data access, human approvals, and failure recovery. Lead complex initiatives, make cross-team architectural decisions, mentor engineers, and balance accuracy, latency, cost, and complexity while remaining hands-on.

Develop technical marketing for AI and agentic solutions by translating engineering specifications into buyer-friendly content. Create technical blogs, case studies, architecture diagrams, demos, comparison materials, FAQs, battlecards, and sales enablement resources. Support technical campaigns, webinars, sales calls, and pre-sales discussions. Research enterprise AI use cases, competitors, and industry trends while collecting customer feedback and analyzing content engagement and business outcomes.

Develops full-stack AI and machine learning software using Python, Java, Vue.js, Node.js, cloud platforms, databases, containers, and CI/CD practices. The role requires software engineering experience on complex programs, testing and debugging capabilities, Agile development experience, DoD8570 IAT2 compliance, and an active TS/SCI clearance with polygraph.

Build and maintain full-stack software products, third-party integrations, public APIs, SDKs, and data connectors. Integrate AI and machine learning capabilities, including LLM, RAG, model-serving, evaluation, guardrails, and monitoring workflows. Partner with data science and clinical teams to productionize models, lead technical projects, conduct design and code reviews, and apply scalable, secure software engineering practices in a healthcare environment.

Leads enterprise AI architecture and delivers production-grade AI platforms and solutions. Designs agentic systems, RAG applications, LLM integrations, evaluations, monitoring, and lifecycle management. Partners with investment, engineering, risk, security, compliance, and business teams to build scalable, governed AI capabilities. Establishes technical standards, embeds controls for regulated environments, drives adoption, and mentors teams through technical influence.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Design, evaluate, and deploy production frontier AI agents that combine LLMs, retrieval, memory, tool use, and traditional ML. Build retrieval and memory systems, multi-agent coordination, evaluation frameworks, and production engineering (reliability, observability, safety). Partner with enterprise customers to translate problems into scalable AI architectures and run experiments to measure business impact.

Develop, optimize, validate, deploy, and monitor AI and machine learning models across cloud, edge, and hybrid environments. Design MLOps pipelines, integrate models with mission and enterprise systems, evaluate architectures, support retraining, and detect model drift. Collaborate with data, cloud, network, and security specialists to deliver reliable, scalable, secure AI capabilities for mission-focused environments.

Design, build, and operate Docker’s enterprise AI agent platform using Go and Kubernetes. Responsibilities include cloud control planes, multi-cloud infrastructure, security and network boundaries, reproducible software supply chains, production ownership, on-call operations, SLOs, incident response, distributed systems design, and technical leadership. The role also includes mentoring engineers and collaborating across runtime, identity, developer experience, and observability teams.

Build AI-powered data agents and production LLM pipelines for analytics and marketing workflows. Rapidly prototype AI applications, select architectures and frameworks, and create seamless AI-assisted user experiences. The role requires backend system architecture, product intuition, customer understanding, and creativity with LLM applications. Responsibilities span proof-of-concept development, production implementation, and end-to-end ownership of AI projects.

Build and own AI-assisted development infrastructure, including autonomous cloud development environments, MCP integrations, agent documentation, internal tooling, and enablement programs. Improve how engineering teams use AI coding agents across the organization, drive cross-team adoption, and stay current with AI productivity trends. The role requires strong software engineering, developer tooling, cloud infrastructure, CI/CD, and application development experience.

Develop applied AI research prototypes and systems for agentic marketing applications, focusing on generative image and video, brand asset creation, data workflows, and autonomous evaluation. The role combines probabilistic and quantitative reasoning, backend system architecture, product intuition, experimentation, and customer-focused product development. Responsibilities include building proofs of concept, improving AI-generated media realism and brand alignment, and architecting agentic systems.

Optimize software workloads across datacenter and agentic AI applications on RISC-V platforms. Responsibilities include profiling applications and runtimes, identifying bottlenecks, tuning compilers and runtimes, analyzing memory and concurrency behavior, leveraging vector, matrix, and custom ISA capabilities, and developing AI-assisted optimization workflows. The role collaborates across software, compiler, hardware, and performance modeling teams to improve throughput, latency, efficiency, and future CPU architecture.