Top AI Engineer Jobs

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

The Frontend Engineer will lead the development of a web application, focusing on architecture, feature development, and testing, while collaborating with other teams and optimizing performance.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

Design and build Machine Learning libraries, implement CI/CD pipelines, and support Ray-based compute environments, collaborating with ML experts.

As a Senior Software Engineer, focus on backend development and AI integration for scientific applications, building systems that leverage advanced AI techniques.

Create developer-focused technical content, demos, reference examples, deployment guides, and docs-as-code workflows to accelerate adoption of NVIDIA enterprise AI software. Work across engineering, product, field, and partners to design developer journeys, build sample applications and notebooks, enable sales and partners, capture feedback, and engage the open source and cloud-native communities.

The Senior AI Software Engineer will design scalable services for AI-driven applications, develop APIs, optimize data pipelines, and ensure system reliability, collaborating with cross-functional teams on edge and cloud AI integration.

The role involves designing and developing scalable services for AI workloads, integrating AI with edge devices, and implementing security best practices. A strong background in backend development and LLM integration is essential.

Design, build, and maintain LLM integrations and AI features; own end-to-end delivery from requirements to production monitoring; ensure scalability, reliability, observability; leverage AI agents to improve developer and operational efficiency; collaborate cross-functionally.

The AI Software Engineer will deploy models, build data pipelines, optimize performance for AI models, and develop testing suites in collaboration with AI scientists.

Design and prototype algorithms for machine learning and reinforcement learning in robotics, focusing on practical problem-solving and real-world application.

Design, build, and maintain secure on‑prem and cloud infrastructure and CI/CD pipelines for Kubernetes-based services. Automate deployments, manage production/staging/dev environments, monitor logs and system health, enforce security controls, and support accreditation (A&A) and operational readiness. Troubleshoot incidents, perform root cause analysis, maintain documentation, and collaborate with Agile teams to drive DevSecOps best practices.

Own product areas end-to-end: discover, prototype, ship, and iterate. Build secure, well-tested, observable full‑stack systems; partner with product, design, data, and clinical teams; use data and experimentation to validate impact; improve AI‑native development practices and engineering systems while balancing speed, safety, and reliability in a regulated healthcare context.

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

Design, build, and operate a production generative/agentic AI platform for autonomous SOC workflows. Lead end-to-end feature development, multi-agent pipeline engineering, observability, security, and incident response in federal environments.

The Senior AI/ML Applications Engineer will develop, integrate, and support AI-driven tools for IC design and verification, focusing on agentic AI methods to enhance productivity.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

The Principal AI Compiler Engineer will design and enhance an AI compiler toolchain, integrating AI for optimizing neural network deployment on SoCs, partner with hardware teams, and improve performance through diagnostics and profiling.

The Full Stack Developer will implement OpenAI and GPT-3 tools to develop processes for creating content and editing media for podcasts.

Lead design and implementation of an AI-native web platform, owning web framework, design system, CMS, CI/CD, infrastructure and SDLC agents. Define architecture, pioneer agentic workflows, set evaluation guardrails, mentor engineers, and drive cross-team technical alignment and migrations.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Own and evolve Syndi, an agent runtime powering in-product chat and integrations. Design, ship, and operate core runtime surfaces (agent loop, tool execution, memory, context), build eval systems, ensure observability and reliability, review consumer integrations, and implement compliance and legal requirements for production LLM systems.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.