Member of Technical Staff, Forward Deployed Engineer
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Before you apply
Salary$200k – $400k
LocationSan Francisco
Work modeon-site
Typefull-time
SeniorityLead
DepartmentResearch & Engineering
EquityIncluded
Company size51–200 people
PostedOct 2, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar
Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes
Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production
Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution
Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries
Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments
High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities
Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems
Experience with NVIDIA or AMD GPUs, CUDA / ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure
Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments
Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams
Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions
Contributed to open-source ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling
Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped code
Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production
Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering
Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements
Skills
Python
Go
TypeScript
Kubernetes
NVIDIA
AMD
CUDA
ROCm
vLLM
SGLang
TensorRT-LLM
TGI
Ray Serve
BentoML
AWS
Benefits
Health insurance
Dental
Vision
401(k)
Overview
Inferact’s mission is to grow vLLM as the world’s AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
About the Role
We’re looking for a Forward Deployed Engineer to make Inferact successful inside real customer environments. You’ll work directly with customers to deploy, integrate, debug, and optimize vLLM-powered inference systems across cloud, Kubernetes, GPU, networking, and model-serving environments.
This is a hands-on engineering role, not a traditional pre-sales position. You’ll move from architecture discussions to implementation, own difficult production problems end-to-end, and work closely with core product and engineering teams to turn what you learn in the field into reusable product capabilities. Your work will directly affect customer time-to-value and how Inferact’s platform evolves.
Skills and Qualifications
Minimum qualifications:
•
Bachelor’s degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
•
Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes.
•
Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production.
•
Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution.
•
Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries.
•
Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments.
•
High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities.
Nice to have
Preferred qualifications:
•
Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems.
•
Experience with NVIDIA or AMD GPUs, CUDA / ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure.
•
Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments.
•
Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams.
•
Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions.
Bonus points if you have:
•
Contributed to open-source ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling.
•
Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped code.
•
Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production.
•
Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering.
•
Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements.
Logistics
•
Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
•
Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
•
Visa sponsorship: We sponsor visas on a case-by-case basis.
•
Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.