Applied AI Inference Engineer - Benchmark & Optimize

CoreWeave; Bellevue (WA); On-site; USD 188,000 - 275,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Flexible Spending Account

Health Savings Account

Tuition Reimbursement

ESPP

Mental Wellness Benefits

Parental Leave

Childcare Support

401(k) Matching

Flexible PTO

Catered Lunch

Job summary

CoreWeave is seeking an Applied AI Engineer to advance the real-world performance of our inference platform. You will build benchmarks, profile model-serving behavior, and drive targeted optimizations for customer workloads across frameworks and hardware.

You will run experiments on quantization, decoding, and routing while collaborating with inference platform engineers to productionize improvements. The role emphasizes applied performance work with measurable impact.

Qualifications

• 4+ years of experience in machine learning, systems, performance engineering, or adjacent applied engineering work.
• Strong programming skills in Python and production engineering environments.
• Experience running empirical evaluations, benchmarks, or experiments and translating results into concrete engineering decisions.
• Familiarity with LLM inference systems and tools such as vLLM, SGLang, TensorRT-LLM, or similar model-serving stacks.
• Understanding of latency, throughput, batching, GPU utilization, quantization, and quality regression analysis.

Responsibilities

• Build and maintain benchmarking workflows that measure latency, throughput, quality regressions, and cost across priority models and serving configurations.
• Benchmark our inference stack against realistic customer workloads and external provider baselines to identify performance gaps and improvement opportunities.
• Profile model-serving behavior across frameworks, runtimes, and hardware configurations to find bottlenecks in prefill, decode, KV cache usage, batching, graph capture, quantization, and related systems.
• Drive targeted optimization efforts for specific customer and product workloads, including tuning serving configurations, evaluating runtime features, and validating changes against representative traces and benchmarks.
• Design and run experiments on model-serving techniques such as quantization, speculative decoding, caching strategies, routing, and other inference optimizations, with careful attention to quality and correctness tradeoffs.
• Partner closely with inference platform engineers to productionize improvements and establish repeatable workflows for performance testing and regression detection.
• Produce clear technical writeups and recommendations that help the team make better decisions about model configurations, runtime choices, hardware allocation, and customer-specific deployment strategies.
• Contribute additional applied research over time as needed to support inference quality, optimization, and product performance goals.

Skills

Python

Benchmarks

Profiling

LLM Inference

Experiments

Tools

Nsight Systems

PyTorch Profiler

Telemetry Pipelines

SGLang

TensorRT-LLM

Job description

CoreWeave is seeking an Applied AI Engineer to advance the real-world performance of our inference platform. You will build benchmarks, profile model-serving behavior, and drive targeted optimizations for customer workloads across frameworks and hardware.

You will run experiments on quantization, decoding, and routing while collaborating with inference platform engineers to productionize improvements. The role emphasizes applied performance work with measurable impact.