Top AI Engineer Jobs

As an AI Infrastructure Engineer, you will manage user-facing services, ensure system reliability, and improve infrastructure using automation tools such as Ansible and Terraform while collaborating with various stakeholders.

Embed with cross-functional teams to map high-value workflows, design automated agentic or deterministic solutions, build production tools and integrations, monitor performance, run evaluations after model changes, and continuously improve AI-driven workflows while communicating impact to stakeholders.

The role involves owning the infrastructure for training and deploying RL-based control systems in humanoid robots, enhancing speed, efficiency, and collaboration among teams.

The Principal Engineer will lead AI-driven productivity initiatives, develop a strategy for integrating AI in engineering workflows, and improve collaboration across teams. Responsibilities include executing projects, ensuring code quality, and setting success metrics.

Design, build, and operate agentic AI systems for Roku TV: prototype, architect, and deploy agents and copilots; implement RAG, tool/function-calling, orchestration, observability, evaluation, and safeguards; integrate agents with backend services and production pipelines; collaborate across teams to deliver scalable, reliable solutions.

As an AI Systems Engineer, you'll design production systems for Codex agents, improve reliability and performance, and collaborate closely with research to enhance model capabilities.

Develop CAD automation pipelines and collaborate with teams to optimize designs for micromachines, ensuring manufacturability and functionality.

Design and implement backend services for an AI-driven automation platform, focusing on LLM orchestration, API development, and system reliability.

Develop real-time Voice AI systems, mentor juniors, and collaborate with customers to optimize call intelligence and reduce latency.

Design and build applications using Python, manage component design, optimize performance, and collaborate with clients on innovative products.

Design, build, and support an AI-ready enterprise data enablement platform using Databricks, dbt, Delta Lake, and Azure data services. Create reusable CI/CD and IaC accelerators, govern data products via Unity Catalog, enable analytical access with Starburst/Trino, troubleshoot production issues, and collaborate with cross-functional teams to deliver governed, reusable, and production-grade data products for analytics and GenAI use cases.

Build and scale AI/ML-enabled features across frontend, backend, and data pipelines. Productionize ML models, deploy MLOps workflows, integrate inference APIs, optimize data models, ensure reliability, and collaborate with cross-functional teams for mission-critical analytics.

The Senior Associate will build and extend the data platform and AI applications, focusing on data engineering, cloud infrastructure, and AI development.

Design, prototype, and deliver enterprise AI solutions (RAG, LLMs, agents). Architect data flows and integrations, evaluate third-party models, build production features, establish telemetry and evaluation, partner with stakeholders, maintain AI roadmap, and evangelize AI adoption across business units.

Lead a small team to research, develop, train, optimize, and deploy computer vision and multimodal fusion AI/ML models for remote sensing (EO/SAR). Analyze large datasets, translate research into operational systems, present technical findings to customers, and produce supporting documentation and user guides.

The role involves developing AI/ML algorithms in various disciplines, analyzing datasets, and mentoring team members within a research environment.

Develop and manage the research infrastructure for AI at Databricks, focusing on large-scale training, orchestration, and improving developer productivity.

Build and maintain benchmarks and evaluation frameworks for document understanding, run experiments, produce weekly technical content (blogs, reports, tutorials), publish analyses comparing models, contribute open-source examples and notebooks, and collaborate with ML teams to highlight capabilities and surface improvements.

Design, develop, and deploy AI-enabled backend services and agentic workflows that integrate ML models, APIs, and retrieval/RAG systems. Build scalable, containerized cloud-native architectures on AWS, implement data pipelines and APIs, ensure performance, reliability, and SDLC best practices, and collaborate with researchers to operationalize machine learning in mission environments.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

The AI Engineer will develop and optimize AI models and high-performance software systems for drug discovery, collaborating with researchers and maintaining infrastructure.

Build and maintain full-stack web and mobile-friendly applications, design and manage Airtable backends, implement workflow automations (n8n/Zapier), integrate LLM/AI services (e.g., Anthropic Claude), optimize performance, collaborate with leadership and vendors, and document systems to reduce single-owner risk.

Design, implement, and optimize embedded firmware and NPU/DSP runtimes for on-device ML. Build realtime sensor-to-inference pipelines, optimize latency, memory, and power, debug hardware/firmware interactions, and collaborate with ML, electrical, and product teams to deliver production-grade edge intelligence.

Build and ship full-stack features for a production AI/LLM platform: implement polished React UIs from specs, own database and access-control/security, integrate multi-provider LLMs (RAG, agentic workflows), and formalize QA by extending test suites with integration and end-to-end coverage.

Design, build, and scale an enterprise AI/ML platform and edge inference systems. Develop FastAPI Python services, CI/CD pipelines, ML orchestration (LangGraph, MLflow), data and knowledge pipelines (RAG, knowledge graphs), deploy models to edge GPUs (NVIDIA Jetson) via Azure IoT Edge, and own reliability, observability, and cost optimization. Hybrid role in Atlanta.