Senior Manager of Engineering, Crusoe Energy Systems

• 2+ years of experience directly managing and leading an engineering team in a high-performance or ML-focused environment
• Strong technical, hands-on experience in software engineering, low-level optimization, or ML infrastructure, with a continued desire to stay close to code and architecture
• A Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field
• Hands-on experience shipping code in production with one or more general-purpose languages, such as Python or C++, with a strong preference for Python
• Familiarity with methods for optimizing LLMs for high throughput / low latency inference
• Comfort with modern LLM serving frameworks such as vLLM or SGLang, and with profiling and analyzing performance down to the kernel level
• A firm grasp of how GPUs are built and how they behave
• Clear interest and hands-on experience with large language models
• A working knowledge of AI/ML pipelines and the full path of developing and deploying ML models
• Strong communication skills, particularly when explaining hard technical topics to customers and teammates

Desirable

• 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 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
• 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 with product and engineering leaders on technical strategy and roadmaps
• Work through ambiguity and make sound calls on tradeoffs and tooling, steering away from complexity that is not needed
• Take real pride and ownership in your work, hold yourself accountable, and look for the same from the people around you