About this opportunity
GMI Cloud lists this Machine Learning Engineer opportunity in mountain view, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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
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Worksite address
mountain view, CA, 94039, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.