Machine Learning Engineer

Location: Bay area (frequent customer interaction) Team: Inference & Reinforcement Learning Platform About the Role We're looking for a Machine Learning Engineer (MLE) to work directly with customers and partners to design, deploy, and validate inference and reinforcement learning (RL) proof-of-concepts on GMI's GPU infrastructure. This is a high-impact, hybrid engineering role that sits at the intersection of platform engineering, applied ML, and customer success. You'll be embedded with customers during early-stage deployments—turning research ideas, datasets, and business requirements into working, performant systems on real GPU clusters. If you enjoy being close to users, debugging real systems, and shipping results fast (not just writing docs), this role is for you. What You'll Do Own customer POCs end-to-end Deploy and optimize LLM inference , RL training , and post-training workflows on GMI clusters Translate customer requirements into concrete system designs and experiments Forward-deploy with customers Work hands-on with research teams, startups, and enterprise customers Debug performance, stability, and correctness issues in real environments Stand up and tune inference stacks (e.g. vLLM / SGLang / Ray Serve–style architectures) Optimize latency, throughput, GPU utilization, and cost efficiency RL & post-training POCs Support RLHF / RFT / SFT workflows using customer-provided datasets Integrate SDKs, training APIs, and cluster resources to shorten "idea ? experiment" cycles Performance & reliability Diagnose GPU, networking, and distributed system bottlenecks Run benchmarks, profiling, and stress tests on multi-GPU / multi-node setups Feedback loop to product Feed real-world customer learnings back into GMI's platform, SDKs, and APIs Help shape reference architectures, cookbooks, and best practices What We're Looking For Core Requirements Strong software engineering background (Python required; Go / Rust a plus) Hands-on experience with ML inference or training systems Familiarity with distributed systems and GPUs (multi-GPU, multi-node) Comfort working directly with customers and ambiguous requirements Ability to debug end-to-end systems (code, infra, networking, performance) Nice to Have Experience with RL or post-training workflows (RLHF, RFT, SFT) PyTorch, DeepSpeed, Megatron-LM, or similar GPU performance profiling and optimization Prior experience as Solutions Engineer Applied Research Engineer What Makes This Role Special You're close to real users and real GPUs —not abstract roadmaps You'll work on cutting-edge inference and RL workloads , not toy demos You'll influence product direction through direct customer feedback Fast iteration, high ownership, and visible impact Who Thrives Here Engineers who like shipping over theorizing People who enjoy being the "last mile" problem solver Builders who want exposure to both deep systems and applied ML Those excited by early-stage POCs that turn into real production systems J-18808-Ljbffr