Software Engineer - Model Performance Systems

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We are looking for Software Engineers to join our team. This is a specialized, high-impact role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will not just be building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure; you will be defining the roadmap, driving key technical decisions, and taking full ownership of the future of this work. RESPONSIBILITIES Benchmarking: Evaluate, run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse, disaggregated serving). DevEx Improvement: Develop and maintain internal GPU-enabled development environments (similar to GitHub Codespaces). You will ensure the team has seaml [... source excerpt omitted ...] ck. Optimization Automation: Build tools to find the "Pareto frontier"—identifying the absolute best configuration (latency vs. cost vs. quality) for a given model and workload. REQUIREMENTS This is a mid-senior, high leverage role. We care about your technical depth, strong communication skills to drive cross-team efforts, ability to navigate vague requirements and mentor other engineers. We want to talk to you if you have: A Love for Systems & Hardware: You aren’t just interested in the AI; you want to understand GPU memory subsystems, InfiniBand, and how data moves across a cluster. An Automation Mindset: You believe that if a task has to be done twice, it should be scripted. [... source excerpt omitted ...] y testing to find the "breaking point" of a system. Mathematical Curiosity: A desire to understand the underlying math of Transformers and how it translates into FLOPs and memory requirements. Technical Toolkit: Familiarity with Python, and an eagerness to master the NVIDIA software stack. C++ familiarity is good to have. WHY THIS ROLE Direct Impact: Your tools will be the gatekeeper for what defines "good" performance for our customers. Deep Learning (Literally): You will gain world-class expertise in GPU orchestration and LLM inference that few engineers in the industry possess. High Ownership: As the lead of a small team, you will have the autonomy to build tools from scratc