Top AI Engineer Jobs

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.

Designs and integrates AI/ML models, agents, and workflows for secure, enterprise-scale mission capabilities. Responsibilities include developing agent orchestration, RAG pipelines, vector database integrations, MCP tool brokers, model routing, evaluation and benchmarking pipelines, observability, governance guardrails, and production AI services. Partners with data scientists and mission analysts to operationalize prototypes and researches emerging AI tooling.

Designs and integrates production AI/ML systems for a secure enterprise AI platform supporting cyber and national mission operations. Responsibilities include building LLM agent workflows, RAG pipelines, vector database integrations, model routing, MCP tool brokers, evaluation frameworks, observability, and governance guardrails. The role partners with data scientists and mission analysts to productionize prototypes and research while evaluating emerging AI technologies.

Develop embedded AI deployment infrastructure and model optimization tools for heterogeneous SoCs. Responsibilities include building end-to-end model deployment workflows, hardware-aware neural architecture design, quantization, pruning, compilation, code generation, and embedded platform integration. The role also explores agentic AI for model-hardware co-optimization and requires collaboration across AI, embedded systems, compiler, and hardware teams.

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, model routing, RAG pipelines, vector databases, MCP tool integrations, evaluation frameworks, and governance guardrails. Partner with data scientists and mission analysts to transition prototypes into secure, enterprise-scale capabilities while evaluating emerging AI technologies and managing operational risks.

Develops human-data capture and integration pipelines for robotics foundation models and reinforcement learning. Responsibilities include reconstructing 3D objects and environments, creating simulation-to-real training environments, designing multimodal policy architectures, evaluating robot foundation models, and transferring human manipulation skills to dexterous hands and humanoid robots.

Build production-quality AI applications using Python and existing large language models. Design multi-agent and multi-step workflows with prompt chaining, tool use, and orchestration; engineer and evaluate prompts; integrate AI into RESTful APIs and microservices; and deploy solutions on AWS. Preferred work includes RAG pipelines, vector databases, observability, exploratory data analysis, and MLOps/LLMOps practices.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Own verification of custom AI accelerator hardware, including matrix and vector engines. Develop SystemVerilog and UVM environments, reference models, assertions, checkers, coverage, and regressions. Verify numerical precision, quantization, data flow, performance, latency, and power efficiency across RTL blocks and complete systems. Collaborate with architecture, software, compiler, runtime, and systems teams through design bring-up, emulation, and pre-silicon validation.

Principal Software Engineer leading architecture and technical strategy for an in-vehicle AI assistant platform. The role spans Android Automotive client applications, cloud services, edge-cloud orchestration, conversational AI, gRPC, streaming interfaces, testing, telemetry, and distributed systems. Responsibilities include cross-functional architectural alignment, roadmap development, resolving complex reliability and privacy trade-offs, integrating AI frameworks, and mentoring senior engineering teams.

Lead architecture and implementation of LangSmith platform features across Go, Python, and TypeScript. Collaborate with product and design teams, mentor engineers, drive project timelines, and deliver clean, tested software. Build and scale APIs and data-intensive applications using backend and frontend technologies, PostgreSQL, and Redis while engaging with enterprise customers and internal stakeholders.

Lead the design and delivery of production machine learning systems for infrastructure observability, troubleshooting, and optimization. Develop anomaly detection, time-series analysis, event correlation, recommendations, ranking, and root-cause inference capabilities. Build evaluation frameworks, data pipelines, feature workflows, model-serving services, monitoring, and feedback loops. Integrate ML with telemetry platforms and customer-facing products, establish engineering standards, mentor engineers, and influence technical roadmaps across teams.

Investigate voice AI campaign and platform issues by analyzing calls, transcripts, logs, configurations, and integration data. Reproduce and document bugs, triage root causes, perform pre-launch and ongoing conversation QA, and develop test plans, regression suites, quality metrics, and automation. Collaborate with engineering, deployment, clinical, and nursing teams to improve agent behavior, workflows, handoffs, scheduling, and outcome capture.

Design and implement AI agents and Model Context Protocols to integrate enterprise systems (NetSuite, Zuora, Jira) with LLMs. Build secure, SOX-compliant automation and orchestration workflows, measure KPIs, and partner with Finance, IT, and Security to operationalize AI across enterprise applications.

The Forward Deployed AI Engineer develops AI solutions using LLMs, collaborates with customers on their AI strategy, and executes high-stakes projects to solve real-world problems.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

Design, develop, test, debug, review, and deploy full-stack applications using Python and Java with Gradle. Gather requirements, resolve defects, create accessible user interfaces, integrate external requirements tools, and explore large language model and AI integrations. Collaborate with customers, product managers, developers, and leadership on project initiatives while mentoring junior engineers. The role is onsite at Fort Meade and supports an Intelligence Community mission.

Build and operate secure, scalable AI compute platform services, including backend APIs, asynchronous workflows, Kubernetes controllers, and multi-tenant workload capabilities. The role focuses on distributed training and inference, platform reliability, observability, developer tooling, and infrastructure simplification. Responsibilities include collaborating across engineering teams, improving scalability and developer productivity, and providing technical leadership for production AI platform capabilities.

Design and build secure, scalable platform services for distributed AI training and inference. Develop backend APIs, Kubernetes controllers, orchestration systems, asynchronous workflows, multi-tenant access controls, and developer tools. Improve reliability, observability, scalability, and engineering productivity while partnering with infrastructure and AI teams. The role also provides technical leadership, mentoring, and cross-functional collaboration throughout platform development and production delivery.

Designs and develops highly available cloud services for AI inference workloads. Responsibilities include building Kubernetes controllers and platform capabilities for deployment, scheduling, scaling, recovery, upgrades, observability, and lifecycle management. The role leads production readiness reviews, resolves complex infrastructure issues, improves reliability and performance, drives architecture and design reviews, mentors engineers, and influences technical direction across teams.

Lead the architecture and hands-on development of distributed AI inference runtime systems. Responsibilities include scheduling, batching, KV-cache and memory management, distributed execution, kernel optimization, profiling, benchmarking, and performance analysis. Enable new model architectures, improve latency and throughput, develop validation and safe-rollout systems, and collaborate with infrastructure, compiler, hardware, cloud, and research teams. Mentor engineers and establish performance-engineering practices for Arm’s production AI platform.

Designs, builds, and operates large-scale AI compute infrastructure for training, fine-tuning, evaluation, and inference. Responsibilities include managing Kubernetes clusters, enabling CPU and GPU systems, optimizing scheduling and capacity, troubleshooting distributed infrastructure, and automating provisioning, upgrades, monitoring, and maintenance. The role partners closely with AI researchers and engineers to improve infrastructure reliability, scalability, performance, and developer productivity.

Lead the architecture and development of highly available cloud services for AI inference. Build Kubernetes controllers and platform capabilities for workload scheduling, scaling, recovery, deployment, and lifecycle management. Improve reliability, observability, performance, and resource efficiency through production readiness reviews and incident-driven improvements. Guide complex technical initiatives, mentor engineers, resolve infrastructure issues, and align architecture across AI compute, runtime, and product teams.

Build foundational platform software, runtime infrastructure, APIs, SDKs, and developer tooling for on-device physical AI workloads. Develop reliable deployment, lifecycle management, observability, and diagnostics systems under edge constraints. Optimize performance across CPUs and AI accelerators while creating stable abstractions over AI frameworks, models, and hardware. Collaborate with architects, product managers, and AI engineers on production-ready platform capabilities, reference implementations, documentation, and workflows.

Design, build, and operate large-scale AI compute infrastructure for training, fine-tuning, evaluation, and inference. Manage Kubernetes clusters, workload scheduling, accelerator enablement, networking, storage, monitoring, and capacity. Troubleshoot complex application, cloud, cluster, and hardware issues while improving reliability, performance, scalability, and developer productivity. Partner with AI teams to automate provisioning, upgrades, monitoring, and maintenance.