Software Engineer - Model Performance Systems

About the role

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 https://www.baseten.co/blog/announcing-our-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 seamless, high-performance "dev machines" optimized for model experimentation.

- Tool Development: Build and contribute to open-source tools such as InferenceMAX and genai-bench to automate model evaluation, benchmarking and analysis.

- System Profiling: Use profilers like PyTorch Profiler, NVIDIA Nsight Systems and py-spy to collect performance profiles, identify bottlenecks, and debug the compute/networking stack.

- Monitoring & Observability: Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance.

- Continuous Integration: Automate performance testing via CI/CD pipelines to catch regressions and build release workflow automation for the model runtimes stack.

- 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. You have a passion for stress-testing and fuzzy testing to find the "breaking point" of a system.