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Lead the design, implementation, rollout, and iteration of Baseten’s identity and authorization platform. Build fine-grained authorization, credential systems, identity lifecycle capabilities, agent credentials, and enterprise admin controls. Ensure low-latency permission checks, consistency, security, observability, and scalability across multi-tenant systems. Partner with product, design, and customer-facing teams, establish engineering practices, and provide technical mentorship as the team grows.

As a Forward Deployed Engineer, you will develop and maintain ML solutions, collaborate with teams on AI/ML pipelines, and manage projects end-to-end.

Lead and mentor a team of Forward Deployed Engineers in building and optimizing LLM inference workloads, delivering high-performance AI applications, and collaborating with cross-functional teams.

The role involves developing and managing infrastructure for distributed LLM inference systems, ensuring performance and usability, debugging complex systems, and collaborating with performance engineers.

As a Software Engineer, you'll build and maintain Baseten's core product, design APIs, implement features, fix issues, and collaborate with teams.

Design and implement RDMA/InfiniBand networking primitives for distributed GPU inference, optimize communication for Disaggregated KV Cache and WideEP, enable sub-10s model startup, validate hardware performance on H100/H200/B200/B300/GB300 clusters, build observability tools, and optimize or author communication kernels (NCCL/NVSHMEM/UCX).

Develop and maintain components for machine learning inference platform. Focus on Kubernetes deployments, resource management, and performance monitoring.

Build and maintain responsive, accessible frontend interfaces using React and TypeScript. Collaborate with product, design, and backend teams to launch performant, scalable UIs, create reusable component libraries, define APIs, mentor engineers, and drive frontend best practices.

Build an internal AI developer platform that provisions agent configs, context tooling, eval harnesses, and rollout mechanics to make AI-assisted engineering the default. Integrate LLMs and third-party coding tools, enable self-serve agent creation, embed safety and audit controls, and measure impact on developer productivity.

The GPU Kernel Engineer will design and implement high-performance GPU kernels, optimize code, and collaborate with research teams to enhance AI workloads.