Role OverviewJoin a small but world-class Applied Research and AI team to work on genuinely hard, open research problems at the intersection of AI and large-scale infrastructure. You will research novel approaches to problems such as optimizing construction across a fleet of simultaneous sites, allocating capital intelligently under deep uncertainty, and keeping a distributed critical infrastructure secure.
What You Will Do
Research novel approaches to problems, collaborate closely with domain experts and engineering partners, and see your work through to deployed systems that actively inform decisions across construction, capital planning, security operations, and beyond.
Why It Might Be a Fit
This is a place to make major contributions on a small but growing team, develop your skills across a remarkable range of hard problems, and be part of something that genuinely matters.
Requirements
- PhD in Computer Science, Machine Learning, Operations Research, Economics, Applied Mathematics, or a closely related quantitative field
- Hands-on experience implementing and evaluating deep RL algorithms
- Fluency in policy gradient methods (PPO, TRPO, SAC), value-based approaches (DQN variants, IQL), and the tradeoffs between model-free and model-based RL
- Experience building RL training environments
- Production-quality Python
- Deep learning frameworks (PyTorch)
- Version control, testing, and reproducibility practices expected of research code that ships into production systems
- Startup Agility: A demonstrated ability to operate in a fast-moving environment where problem definitions evolve, priorities shift, and hands-on technical contribution — not just research direction — is expected at all levels
Benefits
- Competitive compensation packages
- 401k with company match
- Medical, dental, vision plans
- Generous vacation policy, plus holidays
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