About the Role

We’re looking for a Forward Deployment Engineer (FDE) to work directly with customers and partners to design, deploy, and validate Inference dedicated endpoint & Model-as-a-Service products on GMI’s global 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 and multi-modal inference 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

Model-as-a-Service enablement

• Help customers test, evaluate, and adopt the most frontier LLM and multi-modal models through GMI's unified API
• Guide model selection, API integration, and migration across providers; shorten the "idea → production" cycle
• Validate correctness, compatibility, and performance across the MaaS model catalog

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

• Proficiency in at least one programming language (Python and Golang preferred)
• Solid understanding of software systems and distributed systems
• Hands-on experience with ML inference or serving systems
• 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.)
• Global, distributed systems
• Hands-on experience developing and maintaining production services on Kubernetes
• GPU performance profiling, optimization, and inference benchmarking
• Prior experience as:
• Forward Deployed Engineer
• Solutions Engineer
• ML Platform 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 frontier models , 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