Senior Manager, Engineering - AI Inference

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.

About the Role:

As a Senior Engineering Manager, you will lead an engineering team focused on making large language models run faster, cheaper, and more reliably in production. While managing and growing the team, you will stay deeply technical and hands-on with the inference stack end to end: profiling performance, bringing modern optimization techniques into real deployments, and diving into serving code alongside your engineers. This role balances team leadership with core systems and performance engineering on demanding models in production.

The work is applied, not academic. The optimizations you build land in real customer deployments, each with its own models, traffic patterns, latency targets, and cost constraints. So while performance is the heart of the role, you will also work directly with customer engineering teams to tailor deployments to their needs, take a workload from an early proof of concept to a fully monitored production service, and make sure the gains you engineer actually show up for the people running the workload.

To set expectations clearly, this is an engineering leadership role that combines people management, technical direction, and hands-on coding and profiling. It also carries a customer-facing side, along with elements of product and technical solutions leadership, ensuring performance gains deliver clear business and customer value.

What You'll Be Working On:

- Bring current inference techniques into production and refine them.

- Design and optimize serving architectures, including prefill and decode disaggregation, request routing, and related approaches.

- Work down into the serving stack, from frameworks like vLLM and SGLang to the CUDA kernels underneath, profiling and running in-depth analysis to find and fix performance problems.

- Adapt and scale optimization methods across many kinds of ML models, with an emphasis on large language models.

- Profile and tune deployments against clear targets for latency, throughput, and cost, and keep them dependable under real traffic.

- Tailor deployments to each customer's models and constraints, partnering with their engineering teams to move a workload from an early proof of concept through to a live, well-monitored production service.

- Build and support the software and product features around the inference stack in a production setting, using one or more general-purpose languages, with Python preferred given how central it is to ML work.

- Experiment quickly: take fuzzy goals, shape them into clear specs and focused proofs of concept, run fast experiments to find what works, and ship well-tested results without delay.

- Lead team delivery end to end, guiding projects from early experiments to production optimizations, establishing performance goals, and partnering …