About the job
About Hammerhead AI
At Hammerhead AI, we are pioneering the future of artificial intelligence through innovative orchestration, tackling one of the most critical barriers to AI's power accessibility. Our state-of-the-art platform enhances data center power infrastructure to maximize AI token generation within existing electrical constraints, eliminating the need for new power plants or grid extensions. With our team already optimizing over 8 gigawatts of mission-critical power worldwide, we are seizing a $64 billion per year market opportunity while significantly reducing the environmental impact of AI infrastructure.
As part of our dynamic team, you will:
Engage at the confluence of AI, energy, and computing to create the next generation of AI infrastructure
Collaborate with experts in modern reinforcement learning, AI, IoT, and IIoT software, as well as infrastructure technologies
Play a vital role in fostering a more efficient and sustainable future for AI computation.
Join a company at the forefront of advanced data center design and operations
Enjoy competitive compensation, equity, and benefits in a thriving, mission-driven environment.
Learn from a seasoned team with a proven track record of building and selling successful startups.
Role Overview
As a Reinforcement Learning Engineer, you will be the architect behind the core intelligence of Hammerhead’s ORCA platform. Reporting directly to the Head of AI/Reinforcement Learning Engineering, you will be responsible for designing, training, and deploying Orchestrated RL Control Agents that serve as the brain of our system, enabling real-time optimization of power and computational resources across physical data centers. This position is tailored for a hands-on expert passionate about translating advanced RL research into practical applications for complex industrial systems. You will play a crucial role in developing models to control physical assets such as cooling systems and power distribution units, driving substantial efficiency improvements in AI workloads.
Key Responsibilities
- Design, train, and implement reinforcement learning algorithms for power and compute resource optimization.
- Develop and maintain RL models that interact with physical data center assets.
- Collaborate with cross-functional teams to integrate RL solutions into existing infrastructure.
- Continuously monitor and enhance the performance of deployed RL models.
- Stay abreast of the latest developments in reinforcement learning and AI technologies.
