Inference Performance Engineer — Applied AI

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

Benefits offered by this job

Medical, dental, and vision insurance

Equity awards

Discretionary bonus

401(k) with match

Flexible PTO

Tuition reimbursement

Parental leave

Catered lunch

Casual work environment

Job summary

CoreWeave is The Essential Cloud for AI. We are looking for an Applied AI Engineer to help us understand, measure, and improve the real-world performance of our inference platform.

In the near term you will build and run rigorous benchmarks, profile model and system behavior, identify bottlenecks, and drive targeted optimizations for platform-wide and customer-specific workloads. You will work with inference platform engineers to productionize improvements, design experiments on quantization,

Qualifications

• 4+ years of experience in machine learning, systems, performance engineering, or adjacent applied engineering work.
• Strong programming skills in Python and comfort working in 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.

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

Benchmarking

Model serving

Performance tuning

LLM inference

Tools

Nsight Systems

PyTorch profilers

Telemetry pipelines

Benchmark suites

Job description

CoreWeave is The Essential Cloud for AI. We are looking for an Applied AI Engineer to help us understand, measure, and improve the real-world performance of our inference platform.

In the near term you will build and run rigorous benchmarks, profile model and system behavior, identify bottlenecks, and drive targeted optimizations for platform-wide and customer-specific workloads. You will work with inference platform engineers to productionize improvements, design experiments on quantization,