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CoreWeave

Rents GPU cloud computing for AI developers

Senior Software Engineer 1 - Inference

Applied Machine LearningUpdated on Nov 13
Full-timeIn-person

About the Job

Requirements

  • At least 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.
  • Understanding of the practical tradeoffs involved in latency, throughput, batching, GPU utilization, quantization, and quality regression analysis.
  • Ability to work across model, systems, and product boundaries and stay focused on customer-relevant outcomes.
  • Strong written communication and a bias toward making technical work legible and reproducible for others.

Responsibilities

  • Build and maintain benchmarking workflows that measure latency, throughput, quality regressions, and cost across priority models and serving configurations.
  • Benchmark the 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 attention to quality and correctness tradeoffs.
  • Partner with inference platform engineers to productionize improvements and establish repeatable workflows for performance testing and regression detection.
  • Produce technical writeups and recommendations to guide decisions about model configurations, runtime choices, hardware allocation, and customer-specific deployment strategies.
  • Contribute applied research over time to support inference quality, optimization, and product performance goals.

Desired Qualifications

  • Experience optimizing inference workloads on modern GPU hardware.
  • Experience with profiling tools such as Nsight Systems, PyTorch profilers, or custom telemetry pipelines.
  • Familiarity with benchmark suites and evaluation frameworks for coding, reasoning, or agent workloads.
  • Experience using real production traces or customer traffic patterns to guide optimization work.
  • Experience balancing model quality and serving performance when evaluating quantization, speculative decoding, or other acceleration strategies.

About the Company

CoreWeave

Rents GPU cloud computing for AI developers

CoreWeave rents cloud computing to AI labs and businesses that train models and run them in production for research and commercial applications. Its platform combines NVIDIA graphics chips, storage, networking, and software built for demanding AI workloads across connected clusters scaled to fit each project. CoreWeave Forge connects tools for tracking experiments, testing models, and improving AI systems using feedback from production. Customers rent computing capacity through committed contracts or usage charges, while managed services handle the infrastructure needed to keep large workloads running. CoreWeave was founded in 2017 and is headquartered in Livingston, New Jersey.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Livingston, NJ
Founded
2017

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