Staff Applied AI Engineer

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

As an Applied AI Engineer on Managed Inference at Crusoe, you will partner directly with our strategic customers to deploy, optimize, and scale their models into production. You will own the technical journey end to end, from benchmark POCs that win deals to production endpoints serving billions of tokens daily with optimal latency, throughput, and cost. This role bridges deep inference engineering with customer-facing technical leadership, helping shape Crusoe's core AI platform.

• Scope workloads, define metrics (TTFT, throughput, cost/token), build benchmarks, and lead technical POCs.

• Serve as the technical lead for customer engineering teams through architecture reviews and rollout planning.

• Tune engines (KV cache, speculative decoding, parallelism, quantization) 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 to accelerate time-to-value.

• Feed field insights back into product roadmaps and contribute code to our inference engine and tooling.

• Publish reference architectures, cookbooks, and technical blogs.

• 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.

• 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.