Senior Deployment Engineer - LLM Inference & Optimization

Highlights

• Own LLM deployments from discovery and benchmarking through production adoption.
• Help customers achieve measurable gains in model quality, latency, and throughput.
• Run workshops and guide customers through deployment and optimization decisions.

What they offer

• Medical, dental, and vision insurance - 100% paid by CoreWeave
• Company-paid life insurance
• Short and long-term disability insurance
• Flexible Spending Account and Health Savings Account
• Tuition reimbursement
• Employee Stock Purchase Program (ESPP)
• Mental wellness benefits through Spring Health
• Family-forming support through Carrot
• Paid parental leave
• Flexible childcare support with Kinside
• 401(k) with generous employer match
• Flexible PTO
• Catered lunch daily in office and data center locations

Ideal candidate

Hands-on AI engineer who enjoys customer-facing delivery, writing code, diagnosing bottlenecks, and turning ambiguous requirements into measurable production improvements.

Requirements

• 5+ years of experience in software engineering, ML engineering, AI infrastructure, or 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
• Understanding of GPU memory constraints, numerical precision, batching, caching, and parallelism and 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
• Ability to work directly with customers, communicate tradeoffs, and translate business requirements into engineering targets
• Strong ownership and troubleshooting skills, moving from ambiguous problem to tested solution