Forward Deployed Inference Engineer

Acceler8 Talent; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Acceler8 Talent is seeking a Forward Deployed Engineer (Inference) in San Francisco to own customer engagements from initial handoff to production deployment and expansion.

You'll profile, benchmark, trace workloads, design meaningful benchmarks, and work across inference engines, model serving, infrastructure, and observability to keep workloads fast, reliable, and cost-efficient under real user traffic.

Qualifications

• Experience owning problems from investigation to production deployment.
• Ability to work directly with highly technical customers.
• Strong fundamentals in systems engineering and performance profiling.

Responsibilities

• Own customer accounts end to end from handoff to deployment and expansion.
• Profile, benchmark, and trace workloads to estimate performance and infra needs.
• Design benchmarks using real customer models and traffic.
• Explain benchmarking methodology and tradeoffs to engineering teams.
• Implement engineering to bring inference workloads into production.
• Collaborate across engines, model serving, infra, observability, and tooling.
• Maintain production performance and reliability for a portfolio.
• Participate in on-call during latency or reliability incidents.
• Debug production performance issues and drive to resolution.
• Maintain trusted relationships with customer engineering teams.
• Turn recurring customer problems into product improvements.
• Make sound tradeoffs under ambiguity and urgency.

Skills

Systems engineering

Customer engagement

Production deployment

Troubleshooting performance

LLM inference basics

Benchmarking & profiling

Tools

CUDA

Kubernetes

TensorRT-LLM

vLLM

Triton

GPU profiling tools

Job description

Acceler8 Talent is seeking a Forward Deployed Engineer (Inference) in San Francisco to own customer engagements from initial handoff to production deployment and expansion.

You'll profile, benchmark, trace workloads, design meaningful benchmarks, and work across inference engines, model serving, infrastructure, and observability to keep workloads fast, reliable, and cost-efficient under real user traffic.