Top AI Engineer Jobs

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

The AI Infrastructure Engineer will develop and optimize infrastructure for training large language models, focusing on distributed systems and GPU programming.

Drive quality, performance, and engineering effectiveness for Microsoft Edge by designing, implementing, and maintaining diagnostics, telemetry, automation, experimentation, and AI-powered developer tools. Own projects from design through deployment, analyze signals to find improvements, modernize systems for scale and reliability, and collaborate across teams to improve engineering productivity and customer-facing performance.

Architect, build, and operate cloud and on‑prem AI/ML training and inference platforms (including federated learning). Partner with scientists and external collaborators, benchmark and optimize ML infrastructure, ensure security/compliance and cost effectiveness, mentor engineers, and represent the Institute in vendor and cross‑institutional technical engagements.

Build and operate an AWS-native data lake and AI gateway. Implement and maintain lakehouse architecture (Iceberg) using Glue, Athena, and S3; manage infrastructure as code (Terraform); run reliable data pipelines; operate LLM gateway/Bedrock; enforce security, IAM, networking, and secrets controls; partner with IT and Security.

Customer-facing technical role that builds and demonstrates embedded AI capabilities for DoD/IC customers. Translate operator needs into demos, define functional and non-functional requirements, work with integrations/APIs, produce technical proposal content, and support fielding and capture efforts. Requires clearance eligibility and travel to customer/field sites.

Develop and deploy AI/ML algorithms and software for semiconductor inspection, metrology, and process control. Build scalable model training, validation, deployment, and inference systems; optimize performance with GPU acceleration and parallel computing; collaborate across teams to integrate AI into production, cloud, and edge platforms; evaluate emerging AI technologies and deliver proof-of-concepts while maintaining software quality.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Transform AI prototypes into secure, scalable production systems by re-architecting code, designing cloud infrastructure (AWS/Azure), implementing CI/CD, containerizing (Docker/Kubernetes), ensuring security/compliance, building APIs, monitoring, and enabling developer productivity with infrastructure-as-code, reusable templates, and documentation.

Develop, deploy, and own RTL-to-GDSII physical design methodology and CAD flows for IP delivery. Automate PPA exploration, document flows, partner with design teams and EDA vendors, qualify new nodes/PDKs and EDA releases, and apply ML/LLMs where they improve flow optimization and scalability.

Design and validate high-bandwidth interconnects for AI clusters across copper and optical media. Define architectures, perform link and channel budget analysis, lead qualification and reliability testing, develop validation plans for transceivers and cables up to 1.6T, and coordinate with suppliers and manufacturing.

Design, build, and operate enterprise AI platform capabilities including RAG, agentic architectures, vector DB integrations, Bedrock-based model services, AI Gateway governance, observability, security, and developer APIs. Collaborate with security, data, and application teams to ensure scalable, compliant, and cost-optimized AI deployments and operational excellence.

Design and implement AI-enabled workflow orchestration, integrations, and automation pipelines across legal workflows. Build agent orchestration, vector retrieval, API/webhook integrations, monitor reliability and performance, ensure governance and compliance, and collaborate with product, legal, and operations teams to scale AI-native legal service delivery.

Design and implement AI-native workflow orchestration and automation systems for legal service delivery. Build integrations between AI platforms, CRMs, document systems, client portals, and payment systems; implement retrieval, prompt orchestration, event-driven pipelines, and observability. Monitor reliability, troubleshoot bottlenecks, support compliance and governance, and document architectures while evaluating emerging automation tools.

Design, build, and maintain AI-enabled workflow orchestration and automation systems integrating AI models, CRMs, document systems, client portals, and internal tools; implement API/webhook integrations, vector retrieval, prompt orchestration, monitor reliability/performance, and ensure governance, compliance, and scalability.

Design, build, and maintain AI-enabled workflow orchestration and automation systems for legal service delivery. Implement integrations between AI platforms, CRMs, document systems, client portals, and payment systems. Develop event-driven pipelines, retrieval and prompt orchestration, agent workflows, and observability. Monitor reliability, troubleshoot integrations, support compliance and governance, and document architectures and operational standards.

Design and implement AI-native workflow orchestration and automation systems for legal services. Build integrations between AI models, CRMs, document systems, client portals, and operational tools; develop event-driven pipelines, retrieval and prompt orchestration, monitoring, and compliance controls. Collaborate with legal, product, and engineering teams to translate legal workflows into scalable, secure architectures and troubleshoot production issues.

Design and implement AI-native workflow orchestration and automation systems for legal services. Build integrations between AI models, CRMs, document systems, client portals, and internal tools. Develop agent workflows, vector retrieval and observability pipelines, monitor reliability and performance, troubleshoot integrations, and support governance, compliance, and scalable client experiences.

The Senior Fullstack Engineer will lead the design and development of scalable software solutions, enhancing product architecture, collaborating with cross-functional teams, and promoting coding best practices.

Early-career software engineer who writes Python to integrate LLMs and AI services into AWS-hosted applications. Build APIs, data pipelines, embeddings/vector search, and support deployment, testing, and monitoring of AI features while following responsible AI and software engineering best practices.

Design, implement, test, and support highly reliable real-time software for gateways, satellites, user terminals, and switches in a high-capacity satellite network. Lead architecture and code reviews, build prototypes, develop tooling for deployment, data analysis, and testing across virtualized, hardware-in-the-loop, and on-orbit environments, and maintain CI/release systems.

Design, implement, and deploy embedded guidance, navigation, and control systems for satellites including actuators, attitude control, state estimation, and fluid/power system controls. Build and maintain high-fidelity simulations (C++, Python, MATLAB), implement production firmware/software (C++), support hardware sizing, testing, flight software bringup, and fleet performance monitoring.

Design, build, and operate production agentic AI systems on AWS Bedrock AgentCore using Strands/LangGraph. Implement tool integrations (MCP/Gateway), evaluation pipelines and LLM-as-judge CI gates, observability, cost/latency controls, and guardrails for agents operating on live dealer data.

Design and build systems that automate and accelerate research workflows: evaluations, data and evaluation flywheels, agent harnesses, RL environments, experiment orchestration, sandboxed runtimes, integrations with research infrastructure, and metrics to measure research productivity and safety.

Design, build, and deliver enterprise AI solutions using LLMs, cloud platforms, and container orchestration. Own end-to-end delivery, partner with stakeholders, coach junior engineers, and produce production-ready integrations, prototypes, and automations.