Senior Software Engineer I, Inference

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com. About CoreWeave CoreWeave is an AI cloud built for high-performance GPU workloads across AI/ML, VFX, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost efficiency—providing a powerful alternative to traditional hyperscalers. Infrastructure is our product, and we operate it at a massive scale to power the most demanding workloads. About the role We are looking for Senior Software Engineer to help us understand, measure, and improve the real-world performance of our inference platform. In the near term, this role will focus on building and running rigorous benchmarks, profiling model and system behavior, identifying bottlenecks, and driving targeted optimizations for both platform-wide and customer-specific workloads. This role is intentionally scoped around applied performance work in support of the Inference organization. Initial responsibilities center on benchmarking, optimization, and workload-driven research rather than broad ownership of frontier model research agendas. Over time, the scope of the role is expected to broaden as the team and product mature. • 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. Who You Are • 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 outcomes that matter for customers. • Strong written communication and a bias toward making technical work legible and reproducible for others. Preferred • 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. Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: • Be Curious at Your Core • Act Like an Owner • Empower Employees • Deliver Best-in-Class Client Experiences • Achieve More Together We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). What We Offer The range we’ve posted represents the typical compensation range for this role. To det