In this role, you will lead technical content creation, community engagement, and developer outreach across AI infrastructure and managed services workloads. You will produce documentation, tutorials, white papers, sample applications, and video guides.
You will partner with the Product, Engineering, Research, and GTM/Solutions Engineering teams and work across the open-source agent ecosystem.
You will spend most of your time building and then sharing what you build.
What You'll Be Working On
Agentic Reference Systems: Architect and ship end-to-end reference agents and starter kits on Crusoe that developers can use as a starting point for production, covering tool use, MCP, multi-agent orchestration, RAG, structured outputs, memory, and evals.
Examples & Documentation: Own the code examples, tutorials, and documentation that help developers go from first API call to working agent, and keep them tested and up to date as the ecosystem changes.
Open Source & GitHub: Own Crusoe's developer-facing GitHub presence, including examples, cookbooks, and integrations. Contribute upstream to agent frameworks, inference engines, and developer tooling (for example LangGraph, LlamaIndex, CrewAI, Pydantic AI, the OpenAI Agents SDK, MCP, vLLM, and SGLang) so they work well with Crusoe.
Technical Content & Video: Turn the systems you build into content developers rely on: deep-dive blog posts, notebooks, YouTube videos, livestreams, and short technical clips.
Building in Public: Share your work on X, LinkedIn, YouTube, Discord, Reddit, Hacker News, etc. Answer technical questions, review community projects, and become a trusted technical voice in the agent and open-source community.
Use-Case Blueprints: Partner with GTM/Solutions Engineering to spot recurring customer patterns and turn them into open reference architectures for developer segments such as Physical AI, robotics, simulation, and other industry verticals.
Product & Research Partnership: Join Product, Engineering, and Research workflows. Test features and models before launch, make sure samples and docs are ready on launch day, and bring developer friction back as concrete product input, following each issue until it is fixed or explicitly deprioritized.
Talks & Workshops: Give technical talks, live demos, and hands-on labs at conferences, meetups, hackathons, and workshops.
What You'll Bring to the Team
Engineering Experience: 5+ years as a software engineer, ML engineer, applied AI engineer, forward-deployed engineer, or in a similar hands-on engineering role, with strong Python. A Developer Relations title is not required.
Engineering Credibility: Ability to earn credibility with both AI infrastructure engineers and ML/AI engineers.
Agentic AI: Hands-on experience building LLM applications and agents with tool calling, MCP, retrieval, memory, and evaluation, using one or more frameworks such as LangGraph, LlamaIndex, CrewAI, Pydantic AI, or the OpenAI Agents SDK. You have well-formed opinions about what works and what breaks in production.
Cloud Infrastructure: Experience working with cloud computing platforms (such as AWS, GCP, or Azure) or managed AI solutions, with a foundational understanding of cloud infrastructure concepts.
Inference & Model Serving: Working knowledge of how models are served, including OpenAI-compatible APIs, open-weight models, and serving engines such as vLLM or SGLang. You can explain cost and latency tradeoffs credibly.
Public Track Record: Evidence of sharing technical work in public through GitHub repos, open-source contributions, blog posts, videos, talks, or an active technical presence on social platforms. Please include links when you apply.
Technical Communication: Strong communication skills across written and verbal formats, including technical documentation, white papers, video explainers, and presentation decks.
Ownership & Collaboration: You work well across teams, set your own priorities, and ship in a fast-moving environment.
Travel Requirement: Ability to travel up to 20%.
Education: Bachelor's/Master's/PhD degree in Computer Science, or equivalent practical experience.
Bonus Points
Maintainer or significant contributor to an open-source AI, agent, or inference project
Experience producing technical video content such as YouTube tutorials, livestreams, or screencasts
Experience fine-tuning or post-training open-weight models (SFT/RL/LoRA)
An existing technical audience that trusts your views on models, agents, and tooling
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 $180,000-$220,000 + 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.
