Staff Applied AI Engineer

The role

You will partner with strategic customers to deploy, optimize, and scale models into production. You will own the technical journey from benchmark POCs to production endpoints serving billions of tokens daily.

What you will do

• Scope workloads, define metrics, build benchmarks, and lead technical POCs.
• Serve as the technical lead for customer engineering teams through architecture reviews and rollout planning.
• Tune engines to hit aggressive performance targets.
• Profile and diagnose bottlenecks at the engine level to deliver production fixes.
• Own end-to-end customer onboarding, deployment, autoscaling, capacity planning, and SLO monitoring.
• Optimize configurations for base, custom, and fine-tuned models.
• Feed field insights back into product roadmaps and contribute code to the inference engine and tooling.
• Publish reference architectures, cookbooks, and technical blogs.

Required qualifications

• 5+ years in a technical role with strong programming and production skills (Kubernetes, Docker, GPU servers).
• Proven ability to lead technical discussions with customer engineers and leadership.
• Hands-on experience with inference engines (vLLM, SGLang, TensorRT-LLM) and performance profiling.
• Strong command of KV-cache tuning, batching, parallelism, quantization, and related cost math.
• Solid knowledge of the open-source model landscape and hardware-model matching.

Nice to have

• Experience as an FDE, solutions engineer, or inference engineer at an AI cloud, lab, or provider.
• Hands-on background in fine-tuning/post-training (LoRA, SFT, DPO) or contributions to open-source engines.

Benefits

• Pension contributions
• Additional perks to support work-life balance

About the company

Crusoe is a vertically integrated AI infrastructure company that owns and operates each layer of the stack to power AI workloads. They use an energy-first approach to solve AI compute and power bottlenecks.