Senior Manager, Engineering - AI Inference

Crusoe•San Francisco, CA

12d•$250,000 - $300,000

About The Position

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.

Requirements

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.

Nice To Haves

A track record of making software systems run faster, especially for large language models.
Experience with CUDA or comparable technologies.
A strong command of software engineering fundamentals, with a record of building and shipping AI/ML inference systems.
Experience with Docker and Kubernetes.
Prior work building or tuning AI/ML projects, particularly in a customer-facing setting.

Responsibilities

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.