Top AI Engineer Jobs

Designs, builds, tests, and maintains software and data infrastructure for business-facing solutions. Develops CI/CD and DevOps practices, supports applications throughout their lifecycle, contributes to internal tools and standards, and collaborates directly with end users. Requires Python, SQL, large-scale data engineering experience, Azure and Databricks knowledge, and familiarity with Power BI, Copilot Studio, and AI tools.

Build and ship agentic AI product experiences, internal automation, and reliable production AI systems. Partner with research engineers to turn internal workflows into customer-facing features, designing harnesses, execution flows, guardrails, and evaluations. Work end to end across APIs, backend systems, agent orchestration, and frontend interfaces. Identify manual processes suitable for AI automation, resolve customer issues, and operate autonomously in a fast-moving environment.

Own the evaluation system powering a self-improving AI agent loop, advance AI-generated design quality, and develop novel solutions to improve agent performance. The role involves collaborating with product teams, defining priorities, and solving open-ended problems involving AI models, agents, post-training, and context engineering. Candidates should use AI tools regularly, understand AI models or agents, and be strong first-principles thinkers.

Develop and maintain AI-enabled software for Missile Defense Agency customers. Responsibilities include building complex applications, optimization engines, scheduling solutions, graphical interfaces, visualization tools, SQL database integrations, cloud-ready capabilities, and rule-based or machine-learning systems. The engineer will use Agile and DevSecOps practices, integrate COTS, GOTS, and open-source software, modernize legacy tools, and communicate technical concepts to stakeholders.

Design and scale semantic data and AI infrastructure, including embedding pipelines, knowledge graphs, ontologies, vector and hybrid retrieval, RAG systems, and LLM-powered applications. Build production-grade APIs and data pipelines, optimize private GPU inference, establish evaluation and reliability frameworks, and ensure explainability, attribution, and safe outputs. Collaborate across product, analytics, data engineering, and ML teams while mentoring engineers and translating business needs into grounded AI systems.

Build full-stack AI applications for nuclear and energy-sector document analysis and verification. Responsibilities include architecting applications, implementing Retrieval-Augmented Generation systems, developing front-end and backend services, and deploying cloud infrastructure on AWS using ECS, S3, Bedrock, API Gateway, and Route 53. The role requires production software engineering experience, Docker expertise, and collaboration in a fast-paced innovation team.

Develop and optimize scalable backend systems and AI applications, including LLM, GPT, and RAG solutions. Support software project planning, architecture, cross-functional collaboration, deployments, infrastructure, testing, and process improvements. The role requires expertise in TypeScript, Golang, microservices, cloud infrastructure, databases, containerization, DevOps, machine learning frameworks, model optimization, vector databases, and LLM inference stacks.

Build and maintain backend services and APIs powering LangSmith’s AI observability, tracing, monitoring, and evaluation workflows. Responsibilities include architectural design, high-volume data storage and query optimization, reliability engineering, testing, monitoring, alerting, production troubleshooting, root-cause analysis, documentation, and cross-functional feature delivery.

Lead architecture and implementation of LangSmith platform features across Go, Python, and TypeScript. Collaborate with product, design, customers, and internal stakeholders to deliver scalable observability and evaluation capabilities. Write clean, tested code, design high-performance APIs, work with Postgres and Redis, and drive rapid project execution. The role requires strong fullstack engineering, communication, ownership, and customer engagement skills.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Lead design, build, and operate cloud-native AI/ML platform infrastructure and shared services. Architect scalable, secure GPU-enabled training and inference environments, implement IaC and automation, support model deployment/serving pipelines, drive observability and CI/CD, and promote responsible AI practices and enterprise AI-assisted development across teams.

Lead and contribute to design, develop, test, and maintain secure, scalable Java-based enterprise applications. Drive architecture reviews, apply AI-assisted engineering practices, automate remediation, mentor teams, and promote reuse and operational stability across the SDLC.

Serves as an AI-focused Systems Engineering and Technical Assistance expert supporting operational, engineering, and research activities. Provides subject-matter expertise on frontier models, AI economics, foreign and open-source models, emerging AI research, and counter-AI. Works across Intelligence Community components and the Department of War, operating independently and collaboratively in high-pressure environments.

Lead development of an AI-ready research data ecosystem for vaccine R&D: design integrated data architectures, automate ingestion and transformation pipelines, define semantic/metadata frameworks, build knowledge graphs and RAG systems, and develop multimodal predictive and translational ML models. Provide technical leadership, mentor teams, and collaborate across scientists, bioinformatics, and enterprise data organizations to accelerate AI-driven vaccine discovery and decision-making.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

Design and build automation tools, dashboards, and systems to deploy and manage cloud-based infrastructure. Collaborate with cross-functional teams to analyze requirements, implement reliable full-stack solutions (React front-end, Go back-end), improve CI/CD pipelines, and maintain production stability while meeting government background/clearance requirements.

Lead design, build, and operation of AI-powered Growth experiences (conversational onboarding and AI-driven email). Own technical strategy for an AI Voice Agent and ACE-powered email systems, design APIs and frontend/backends, partner cross-functionally, establish applied-AI engineering patterns, measure impact, and mentor engineers to improve product quality and responsible AI use.

Drive company-wide AI adoption by embedding with cross-functional teams to identify use cases and build production AI workflows, agents, internal tools, prompt/skill libraries, evaluation frameworks, and safe deployment patterns. Provide training, office hours, and enablement while partnering with security, IT, legal, and clinical stakeholders to ensure PHI-safe guardrails and measure adoption, productivity, and quality improvements.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Configure, integrate, and deploy the BT tool stack (Snowflake, Salesforce, Streamlit, workflow tools). Build integrations and Streamlit apps, deploy AI-enabled automations, maintain Snowflake data models and reports, catalog assets with DataHub, implement SDLC/testing/guardrails, ensure data security, and partner with Engineering/InfoSec while mentoring BT team members.

Lead deployment, inference optimization, resource scheduling, profiling, and stability initiatives for AI models across autonomous vehicles and cloud infrastructure. Design low-latency inference pipelines, optimize GPU execution and memory usage, support LLM serving and model validation, diagnose software-hardware integration issues, and collaborate with perception, prediction, cloud infrastructure, and safety teams.

Develop computer vision and vision-language models for image and video understanding in restaurant environments. Build datasets, training strategies, evaluations, and benchmarks; diagnose model failures; and deploy efficient, reliable inference pipelines. The role combines research and engineering across scene understanding, object tracking, activity recognition, temporal reasoning, visual grounding, and multimodal models, while partnering with product and infrastructure teams to bring research into production.

Leads development of AI-powered tools, simulation environments, automation, and learning-based systems that accelerate firmware and controls engineering for powered athlete products. Builds coding assistants, sim-to-real workflows, deployment frameworks, optimization systems, data pipelines, and regression analysis capabilities. Partners with firmware, controls, hardware, and product teams to improve development speed, quality, and reliability across complex mechatronic systems.

Build reinforcement learning environments, evaluations, reward functions, and tool-using agents for chip-design tasks. Design and analyze experiments, train models, investigate failures, improve evaluation and research infrastructure, and turn successful approaches into reusable code and workflows. Collaborate with AI researchers, software engineers, and hardware specialists on RTL generation, verification, and physical-design optimization while ensuring correctness and measurable performance.

Build and deploy internal AI tooling, agents, integrations, and workflows that improve productivity across engineering, research, and business teams. Evaluate emerging AI tools and models, develop observability and responsible-use guardrails, and partner with stakeholders to prototype and scale solutions. Drive adoption through documentation, enablement, and hands-on support while integrating AI into autonomy, simulation, ML training, and business workflows.