Engineering Manager – Applied AI

General Matter•Los Angeles, CA

About The Position

General Matter enriches uranium. We are designing, building, and operating the world’s lowest-cost enrichment services, here in the US. In the process, we are restoring America’s ability to produce nuclear fuel to power AI, advanced manufacturing and critical industries our country relies on to remain competitive. Fuel drives the cost of advanced reactor electricity production, and enrichment drives the cost of the fuel that advanced reactors consume. Reliable, low-cost enrichment is the catalyst for the nuclear Renaissance now under way. Our mission is to make nuclear not only the cleanest and safest source of baseload power, but also the most affordable. We believe abundant nuclear energy will lead to a post-scarcity society. We were incubated inside Founders Fund, like Anduril and Palantir before us, and are backed by over a dozen of the world's top venture capital firms. Our lean, world-class team of engineers and operators is applying a first-principles approach to solving the problem of nuclear fuel production. We are a mission-driven company with a culture of urgency, accountability, and transparency. Help us build a high-energy society by making the cleanest, safest form of baseload energy the most affordable. General Matter has a massive body of critical-path engineering work ahead — nuclear licensing, integrated safety analysis, design criteria, technoeconomic modeling. Conventional timelines for this work are too slow. We believe there is an opportunity to reinvent every single engineering workflow using modern AI. The Applied AI team embeds with engineering teams across the company with a single focus: make existing engineering workflows significantly faster. You identify the bottleneck, build the tool, and move to the next problem. This is an opportunity to define what AI-first engineering looks like at a nuclear company. The nuclear industry has done things the same way for decades. Assumptions that everyone takes for granted — “this analysis requires a PhD specialist,” “this document takes three months to draft,” “this review has to be fully manual” — are all open questions now. You get to challenge every one of them, across every team.

Requirements

7+ years of professional software engineering or applied AI experience.
2+ years of experience managing, mentoring, or growing software or applied AI engineers.
Demonstrated experience building and shipping software using LLMs, AI agents, or other modern AI techniques.
Strong software engineering fundamentals and proficiency in Python.
BS or higher in engineering, physics, chemistry, applied math, computer science, or a related technical field.

Nice To Haves

Technical leader who still codes: You enjoy reviewing PRs, debating architecture, and jumping into difficult technical problems. This is not a role you can do from a distance.
AI fluency: You have hands-on experience with technologies such as LLMs, RAG, tool-use architectures, and AI agents, and think critically about where AI can create meaningful leverage.
Strong engineering judgment: You can quickly understand technical problems across disciplines and evaluate whether your team’s approach is sound without needing to be the domain expert.
Workflow-oriented mindset: You’re excited by finding ways to fundamentally improve how engineering teams work, rather than simply adding AI to existing processes.
Builds high-agency teams: You hire and develop engineers who take ownership, move quickly, and solve problems without waiting for a detailed roadmap or specification.
Experience scaling small teams: You’ve helped a high-context engineering team add structure and process while preserving speed, autonomy, and technical quality

Responsibilities

Lead and grow the Applied AI team: Hire, develop, and retain engineers while setting a high technical bar.
Own technical direction and prioritization: Identify the highest-leverage engineering problems and decide where the team should invest its time.
Stay hands-on: Review architecture, solve difficult technical problems, and contribute to implementation when appropriate.
Partner with engineering teams across General Matter: Work directly with licensing, safety, design, manufacturing, and other technical teams to understand their workflows and identify opportunities for AI-driven leverage.
Build scalable engineering practices: Establish the processes for project selection, prototyping, evaluation, and production handoff that allow the team to grow without losing speed.
Represent Applied AI across the company: Communicate technical direction, progress, and tradeoffs to company leadership and cross-functional partners.