Forward Deployment Engineer

We’re looking for a Forward Deployment Engineer (FDE) 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., 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

Inference deployment

• 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

Do you have experience in Technical troubleshooting support?, * 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:
• LLM inference frameworks (vLLM, SGLang, Ray Serve, Triton, etc.)
• RL or post-training workflows (RLHF, RFT, SFT)
• PyTorch, DeepSpeed, Megatron-LM, or similar
• Kubernetes-based ML platforms
• GPU performance profiling and optimization
• Prior experience as:
• Forward Deployed Engineer
• Solutions Engineer
• ML Platform Engineer
• Applied Research Engineer, * 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

$100,000 - $200,000 a year - Full-time