---
title: 'Staff Applied AI Inference Engineer at Crusoe'
canonical: 'https://feeny.ai/job/staff-applied-ai-inference-engineer-crusoe-san-francisco-8e02xv89vt3j'
type: 'job'
last_seen: '2026-09-11'
---

# Staff Applied AI Inference Engineer at Crusoe

- **Company:** [Crusoe](https://feeny.ai/companies/crusoe)
- **Location:** San Francisco, CA / United States
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-21
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.ashbyhq.com/crusoe/933bcbb7-14f6-4058-8f28-d4354f507ed2

## Job description

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

You will spend your time making large language models run faster, cheaper, and more reliably in production. That means owning the inference stack end to end: profiling where time and cost go, bringing modern optimization techniques into real deployments, and getting deep into the serving code when the defaults are not good enough. This is core systems and performance work on some of the most demanding models in use today.

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 a hands-on engineering role built around coding, profiling, and low-level optimization. It also carries a customer-facing side, along with elements of product and technical solutions work, because that is where the performance work gets proven.

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.
- Own delivery end to end, from the first experiment through to the optimization running in production, keeping the underlying performance goals, clear specs, and follow-through front of mind, and drafting features and product requirement documents together with other engineering and product teams.
- 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.

What You'll Bring to the Team:

- 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.

Bonus points:

- 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.

Benefits:

- Competitive compensation and equity packages
- Restricted Stock Units
- Paid time off, paid holidays & leave of absence programs
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
- Global travel insurance & emergency assistance
- Daily meals allowance
- Additional perks & programs specific to location

## Compensation Range

Compensation will be paid in the range of up to $215,000 - $260,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.

Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

## About Crusoe

## Company Overview
- **One-liner**: Crusoe is an energy-first AI infrastructure company that designs, builds, and operates hyperscale data centers and a cloud platform purpose-built for artificial intelligence workloads.
- **Entity Type**: Private (Series E)
- **Headquarters**: Denver, Colorado, USA
- **Founded**: 2018
- **Founders**: Chase Lochmiller and Cully Cavness

## Core Business
- Primary industry/industries: AI Infrastructure, Cloud Computing, Data Centers, Clean Energy
- Target customers: Enterprise AI companies, AI-native startups, research institutions, government agencies (B2B)
- **Mission**: "Accelerate the abundance of energy and intelligence."

## Products & Services
- **Crusoe Cloud**: An AI-optimized cloud platform purpose-built for AI workloads, featuring high-performance NVIDIA and AMD compute, accelerated storage, and optimized RDMA networking. Claims to deploy models up to 20x faster and cut costs by up to 81%.
- **Crusoe Managed Inference**: A managed AI inference service built with Crusoe's proprietary MemoryAlloy technology, offering best-in-class speed, throughput, and reliability. Features the Crusoe Intelligence Foundry for model selection and API key management.
- **AI Data Center Infrastructure**: Vertically integrated design, build, and operation of hyperscale AI factories (e.g., the 1.2 GW Stargate campus in Abilene, TX). Modular, scalable construction for power-hungry AI workloads.
- **Crusoe Managed Kubernetes & Slurm**: Managed orchestration services that eliminate operational overhead for customers.
- **Crusoe AutoClusters**: Fault-tolerant, managed cluster services for large-scale AI training.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (private company).
- **Key Metric**: Raised over $1.8B+ in total funding (Series E in 2024 was $1B+; Series D in 2024 was $600M; Series C in 2021 was $350M).
- **Notable Investors/Partners**: NVIDIA, NYDIG, GE Vernova, Redwood Materials, Tallgrass, AMD. The company has also partnered with leading energy and technology firms to power its data centers.
- **Growth Signals**: Divested bitcoin mining business to NYDIG in 2023 to focus exclusively on AI infrastructure; broke ground on the 1.2 GW Stargate campus in Abilene, TX; expanded into Europe with a geothermal/hydro-powered deployment in Iceland; announced 1.8 GW Wyoming data center with Tallgrass; acquired GPU memory optimization startup Atero and established a Tel Aviv office.

## Competitive Advantages
- **Energy-First Approach**: The company’s core differentiator is its vertical integration starting from energy sourcing. It uses "environmentally aligned power sources" (wind, solar, hydropower, geothermal, natural gas with carbon capture) to power its AI factories, including pioneering technology to convert wasted natural gas into energy.
- **Vertical Integration**: Controls the entire stack—from finding innovative energy sources to building and managing hyperscale data centers and operating the cloud platform.
- **NVIDIA Partnership**: Close partnership with NVIDIA, providing access to the latest and highest-performance GPU hardware for its cloud and infrastructure.
- **Proven Performance**: Claims 99.98% uptime, 100% customer satisfaction score, and significant cost/performance advantages (up to 81% cost reduction).

## Strategic Focus
- **Massive Infrastructure Expansion**: Aggressively scaling its data center footprint, with major projects in Abilene, Texas (Stargate) and Wyoming (Tallgrass), and expansion into Europe.
- **Deepening AI Cloud**: Continuing to build out its Crusoe Cloud platform with managed services (Kubernetes, Slurm, AutoClusters) to make it easier for AI companies to use their infrastructure.
- **Responsible AI**: Emphasizing sustainability and responsible AI governance, as evidenced by achieving ISO 42001 certification and publishing an ESG report.
- **Acquisition-led Growth**: Acquired Atero (GPU memory optimization) to enhance its core technology stack.

## Why Work Here
- **Mission-Driven**: The company is united by a tangible mission: "Accelerate the abundance of energy and intelligence." Employees report high levels of engagement (92% proud to work there per internal survey).
- **Massive Growth Trajectory**: Crusoe is at the center of the AI infrastructure boom, building some of the world's most powerful AI factories. For job seekers, this means exposure to cutting-edge technology and the opportunity to scale a company from a startup to an industry leader.
- **Culture**: The company values "moving fast and making things," continuous learning, and collective genius. It describes itself as an "AI factory company" that values velocity and impact.
- **Work Policy**: Hybrid/Office presence required in most roles. Office locations include: Denver (HQ), San Francisco, Sunnyvale, Bellevue, Tulsa, Dublin (Ireland), and Tel Aviv (Israel).
- **Benefits**: Competitive compensation & equity, comprehensive health & wellness, mental health resources, retirement plans, paid time off, leave of absence programs, learning & development, volunteer time off, daily meals allowance, and other location-specific perks.
- **Engineering Culture**: Strong emphasis on technical excellence, working with large-scale systems, and pioneering new approaches to energy and infrastructure. The company values candidates' "AI fluency" in its hiring process.

## Sources
1. [crusoe.ai](https://www.crusoe.ai/)
2. [crusoe.ai/careers](https://www.crusoe.ai/about/careers)
3. [crusoe.ai/about/culture](https://www.crusoe.ai/about/careers/culture)
4. [crusoe.ai/about/company](https://www.crusoe.ai/about/company)

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