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 $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.

THE OPPORTUNITY

We are looking for early-career Software Engineers to join our team in Vancouver, BC. This is a specialized role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will be responsible for building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure.

In this role, you won’t just be using models; you will be tearing them apart to see how they run on the metal. You will build tools that measure GPU FLOPS, stress-test InfiniBand clusters, and define the benchmarks that ensure our systems are production-ready.

RESPONSIBILITIES

- Performance Benchmarking: Run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse).

- Infrastructure Validation: Create automated acceptance tests for new GPU clusters across x86 and ARM systems, measuring GPU memory bandwidth, networking throughput, and multi-node networking performance.

- Model Dev Experience: 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 tools such as InferenceMAX and genai-bench to automate model evaluation and optimization.

- Deep Hardware Profiling: Use PyTorch Profiler and NVIDIA Nsight Systems to collect performance profiles, identify bottlenecks, and debug the NVIDIA 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 in model setups before they hit production.

- 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.

WHAT WE'RE LOOKING FOR

This is a fresher-friendly role. We care more about your trajectory, curiosity, and technical depth than your years of experience. 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