Software Engineer, Model Performance Tooling at Baseten · New York

Summary: Baseten is a company that powers mission-critical inference for leading AI companies, and they are seeking early-career Software Engineers to join their team. The role involves building automated tools for performance benchmarking and infrastructure validation in high-performance computing and large language model engineering. Responsibilities: - Run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse) - Create automated acceptance tests for new GPU clusters across x86 and ARM systems, measuring GPU memory bandwidth, networking throughput, and multi-node networking performance - 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 - Build and contribute to tools such as InferenceMAX and genai-bench to automate model evaluation and optimization - Use PyTorch Profiler and NVIDIA Nsight Systems to collect performance profiles, identify bottlenecks, and debug the NVIDIA compute/networking stack - Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance - Automate performance testing via CI/CD pipelines to catch regressions in model setups before they hit production - Build tools to find the "Pareto frontier"—identifying the absolute best configuration (latency vs. cost vs. quality) for a given model and workload Required Qualifications: - 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 - Mathematical Curiosity: A desire to understand the underlying math of Transformers and how it translates into FLOPs and memory requirements - Interest in Optimization: You are excited to learn about (or already play with) quantization, speculative decoding, disaggregated serving, and kernel-level optimizations - Technical Toolkit: Familiarity with Python, and an eagerness to master the NVIDIA software stack. C++ familiarity is good to have Required Skills: GPU performance profiling, Performance benchmarking, Automated testing Important Skills: Python, NVIDIA software stack Nice-to-Have Skills: C++ familiarity, Mathematical curiosity, Automation mindset, Interest in optimization, Love for systems & hardware Benefits: Competitive compensation, including meaningful equity., 100% coverage of medical, dental, and vision insurance for employee and dependents, Generous PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!), Paid parental leave, Company-facilitated 401(k), Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.