About this opportunity
Acceler8 Talent lists this Machine Learning Engineer (Inference) opportunity in san francisco, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
ML Inference Engineer
Build the infrastructure that makes cutting-edge AI fast enough to work at scale.
We’re hiring anML Inference Engineer for a Stanford-spun AI startup in San Francisco that has already grown to8-figure revenue .
The team is rebuilding itsLLM inference stack from the ground up , solving challenging systems problems around GPU performance, distributed compute and real-time model serving.
This role is for engineers who enjoy going deep onperformance, infrastructure, and optimisation .
The role
Build the infrastructure that serveslarge-scale LLM workloads
Push the limits oflatency, throughput and GPU efficiency
Design distributed inference acrosssingle and multi-GPU systems
Improve GPU scheduling, orchestration and resource utilisation
Profile and remove bottlenecks acrosscompute, memory and networking
Scale production workloads acrossKubernetes and GPU clusters
Make low-level architecture decisions wheremilliseconds matter
What we're looking for
StrongPython and/or C++
Experience withdistributed systems or high-performance computing
Knowledge ofLLM inference and model serving
Experience optimising GPU-heavy workloads
Exposure toCUDA, NCCL or Triton
Strong understanding ofPyTorch and modern ML infrastructure
Experience withvLLM, TensorRT-LLM, SGLang or similar
Knowledge of techniques such asquantisation, batching, KV caching and parallelism
Why join?
Tackle genuinely difficultAI infrastructure problems
Work on systems operating atreal production scale
Join a fast-growing company already at8-figure revenue
Significant ownership over anew inference architecture
Work at the intersection ofLLMs, GPUs and distributed systems
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Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.