Top AI & Machine Learning Jobs

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

Lead multi-year product strategy and roadmap for DigitalOcean's Data and AI platform. Own development of vector databases, knowledge bases, Iceberg-supported data primitives, and integrations for RAG and AI agents. Drive cross-functional execution, pricing, capacity planning, and customer-driven feature prioritization; measure utilization, reliability, and deployment velocity.

Lead the strategy and execution for DigitalOcean's agentic AI infrastructure: design and scale runtimes, sandboxed execution, tool and state management, launch revenue-driving SKUs, optimize efficiency and margins, and build a high-performing distributed engineering organization focused on reliability and operational excellence.

Own product strategy and roadmap for managed agent infrastructure. Define services (harness server, sandbox, gateways), partner with customers and engineering, shape developer experiences, launch products, and drive adoption, reliability, and revenue metrics.

Lead-capable engineer who designs, ships, and operates production AI/agent systems and internal AI platform services. Own multi-person workstreams, set technical direction, ensure evaluation, observability, safety, and cost controls, partner with business owners to translate ambiguous problems into measurable solutions, and mentor engineers to raise team engineering standards.

Join the AI Engineering team to implement features for agents, copilots, and internal AI platform services. Write clean, tested code, integrate tools and data sources, build evaluation harnesses, and apply modern AI engineering practices (prompting, RAG, tool use). Collaborate with stakeholders, participate in code reviews, design discussions, and on-call rotation while growing ownership and fluency in agentic systems.

Design, build, and ship agentic systems, copilots, and platform components end-to-end. Implement orchestration, retrieval, memory, tool use, evaluation, observability, and on-call readiness. Partner with stakeholders, mentor junior engineers, and improve internal AI platform tooling and evaluations for production reliability.

Design, build, and operate the Action Runtime execution layer for AI agents. Own full-stack product features from design through production, including MCP tooling, authentication, secrets, rate limiting, observability, and safe execution. Collaborate with product, design, and cross-functional teams, mentor peers, participate in on-call, lead incident responses, and define quality standards, evaluations, and telemetry for reliable agent tooling.

Lead strategic partnerships with AI model developers and cloud technology providers. Identify market opportunities, negotiate complex commercial agreements (OEM, revenue share), and coordinate cross-functional execution with Product, Engineering, Finance, Legal, and GTM teams to expand DigitalOcean's AI‑Native Cloud capabilities.

Lead and grow an engineering team to design, build, and scale a production LLM inference platform. Drive architecture, scheduling, GPU utilization, orchestration, observability, security, and cross-functional delivery for large Kubernetes clusters and multi-tenant AI workloads.