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 the Team

Senior engineers are area owners who lead designs, raise engineering standards, and deliver measurable improvements to latency, throughput, and reliability across multiple services. You'll partner with product, orchestration, and hardware teams to evolve our Kubernetes-native inference platform and meet strict P99 SLAs at scale.

Key Responsibilities

Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.
Define and own SLIs/SLOs; ensure post-incident actions land and reliability improves release-over-release.
Implement advanced optimizations (e.g., micro-batch schedulers, speculative decoding, KV-cache reuse) and quantify impact.
Strengthen incident posture: capacity planning, autoscaling policy, graceful degradation, rollback/traffic-shift strategies.
Mentor IC1/IC2 engineers; review cross-team designs and elevate coding/testing standards.
For IC4: own an area spanning multiple services and teams (e.g., request routing & adaptive scheduling, cost-per-token analytics, GPU resource isolation).

Qualifications

IC3: ~3–5 years; IC4: ~5–8 years industry experience building distributed systems or cloud services.
Strong coding in Python or Go (C++ a plus) and deep familiarity with networked systems and performance.
Hands-on experience with Kubernetes at production scale, CI/CD, and observability stacks (Prometheus, Grafana, OpenTelemetry).
Practical knowledge of inference internals: batching, caching, mixed precision (BF16/FP8), streaming token delivery.
Proven track record improving tail latency (P95/P99) and service reliability through metrics-driven work.
Bachelor's or Master's in Computer Science or related field (or equivalent practical experience).

Preferred Skills

Contributions to inference frameworks (vLLM, Triton, TensorRT-LLM, Ray Serve, TorchServe).
Experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies.
Leading multi-team initiatives or partnering with customers on mission-critical launches.

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 enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities 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 $182,000 to $242,000. The starting salary will be determined by job-related knowledge, skills, experience, and the 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

In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include: