Senior Deployment Engineer - LLM Inference & Optimization

We’re looking for a Deployment Engineer who combines hands-on AI engineering, model optimization, and strong customer-facing skills. You’ll work directly with customers to bring AI models and applications into production, improve their performance, and integrate them with enterprise systems. You’ll own technical delivery from discovery and benchmarking through deployment and adoption, working closely with customer stakeholders and our product and engineering teams.

This role requires someone who enjoys writing code, experimenting with models, diagnosing performance bottlenecks, and helping customers achieve measurable improvements in model quality, latency, throughput, and cost.

• Understand customer requirements: Lead technical discovery to define use cases, workloads, infrastructure constraints, and targets for model quality, latency, throughput, and cost.
• Optimize inference: Apply techniques such as quantization, batching, prefix caching, KV-cache optimization, and parallelism to improve performance while preserving output quality.
• Adapt models to customer needs: Improve results through prompt optimization, retrieval-augmented generation, and fine-tuning where appropriate. Evaluate the impact of each approach.
• Troubleshoot performance: Investigate issues across model execution, GPU utilization, memory consumption, serving infrastructure, and application behavior.
• Lead technical customer engagement: Run workshops, present benchmark findings, explain tradeoffs, and guide customers through deployment and optimization decisions.
• Build reusable capabilities: Create evaluation tools, benchmark harnesses, deployment configurations, and runbooks. Bring recurring customer needs and performance findings back to product and engineering.

• 5+ years of experience in software engineering, ML engineering, AI infrastructure, or a related hands-on technical role.
• Strong Python programming skills and experience with frameworks such as PyTorch, Hugging Face Transformers, or comparable tools.
• Hands-on experience deploying and optimizing AI models in production, particularly LLMs.
• Experience with model-serving frameworks such as vLLM, SGLang, TensorRT-LLM, or similar technologies.
• An understanding of GPU memory constraints, numerical precision, batching, caching, and parallelism, including how these affect inference performance.
• Experience building evaluations and benchmarks to measure model quality, latency, throughput, and resource efficiency.
• Experience developing APIs and integrating model endpoints with production applications and customer data systems.
• Familiarity with GPU-based cloud environments, containers, and monitoring tools used to operate model deployments.
• The ability to work directly with customers, communicate technical tradeoffs clearly, and translate business requirements into measurable engineering targets.
• Strong ownership and troubleshooting skills, with the ability to move from an ambiguous problem to a tested solution.

• Experience with parameter-efficient fine-tuning techniques such as LoRA or QLoRA.
• Experience with speculative decoding, multi-GPU inference, or distributed model serving.
• Experience profiling GPU workloads using CUDA, NVIDIA Nsight, or related tools.
• Experience optimizing embedding models, rerankers, vision models, or multimodal systems.
• Experience improving retrieval quality and evaluating end-to-end RAG applications.
• Contributions to open-source model-serving, optimization, or evaluation projects.
• Experience delivering enterprise AI implementations or customer-facing technical engagements.

What success looks like You help customers deploy AI solutions that meet agreed quality and performance targets. Your work produces measurable improvements in latency, throughput, GPU utilization, and cost while maintaining model quality. Customers understand the tradeoffs, trust your technical guidance, and can operate the solutions you deliver. Your findings and tools make future deployments faster and more effective.

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:

• Medical, dental, and vision insurance – 100% paid for by CoreWeave
• Company-paid Life Insurance
• Voluntary supplemental life insurance
• Short and long-term disability insurance
• Flexible Spending Account
• Health Savings Account
• Tuition Reimbursement
• Ability to Participate in Employee Stock Purchase Program (ESPP)
• Mental Wellness Benefits through Spring Health
• Family-Forming support provided by Carrot
• Paid Parental Leave
• Flexible, full-service childcare support with Kinside
• 401(k) with a generous employer match
• Flexible PTO
• Catered lunch each day in our office and data center locations
• A casual work environment
• A work culture focused on innovative disruption

California Applicants

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.