Reinforcement Learning Engineer

About Hammerhead

We're unleashing AI with intelligent orchestration while addressing one of the most pressing bottlenecks for AI access to Power. Our cutting-edge platform optimizes data center power infrastructure to maximize AI token generation within existing electrical limits, without requiring new power plants or grid expansions. Our team has optimized over 8 gigawatts of mission-critical power globally, and we're addressing a $64 billion per- year market opportunity while dramatically reducing the environmental footprint of AI infrastructure.

At Hammerhead, you will:

• Work at the intersection of AI, energy, and compute creating the next generation AI infrastructure
• Collaborate with colleagues that are experts in modern RL and AI, IoT and IIoT software, and infrastructure technologies
• Contribute to building a more efficient and sustainable future for AI compute.
• Join a company at the cutting edge of modern data center design and operation
• Receive competitive compensation, equity, and benefits in a high-growth, mission-driven environment.

Learn from an experienced team that has built and sold startups before.

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As a Reinforcement Learning Engineer, you will be the architect of the core intelligence for Hammerhead’s ORCA platform. Reporting to the Head of AI / Reinforcement Learning Engineering, you will design, train, and deploy the Orchestrated RL Control Agents that form the brain of our system, making real-time decisions to optimize power and compute resources across physical data centers. This role is for a hands-on expert who is passionate about applying cutting-edge RL research to complex, real-world industrial systems.

You will be instrumental in developing the models that control physical assets like cooling systems and power distribution units to unlock massive efficiency gains in AI workloads.

• RL Model Development:
  Design and implement advanced reinforcement learning algorithms (e.g., multi-agent RL, model-based RL, deep RL) for real-time control of data center infrastructure.
• Simulation and Training:
  Build and train RL agents that can generalize to real-world, physical systems.
• From Lab to Production:
  Lead the transition of RL models from research and simulation to live deployment within the ORCA platform, ensuring stability and performance on mission-critical hardware.
• System Optimization:
  Analyze agent performance to continuously improve control strategies for tasks like peak shaving, workload shifting, and thermal management.
• Cross-Functional Collaboration:
  
  Partner with platform engineers to define the APIs, data telemetry, and infrastructure needed to support and scale our RL agents across a global portfolio of data centers.

• RL Expertise:
  Proven experience developing and implementing reinforcement learning algorithms, demonstrated through publications in top conferences (e.g., NeurIPS, ICML, ICLR), open-source contributions, or shipped products.
• Industry
  
  Experience:
  
  3+ years of experience applying RL to real-world problems, preferably in industrial automation, robotics, autonomous vehicles, energy systems, or other physical systems. Experience from a leading industrial or academic RL lab is highly desirable.
• Technical
  
  Skills:
  
  Deep proficiency in Python and modern ML frameworks such as PyTorch, Jax, or Tensor Flow. Experience with simulation platforms and RL libraries (e.g., Ray RLlib, Isaac Gym) is a plus.
• Educational Background: MS or PhD in Computer Science, Robotics, Operations Research, or a related field with a focus on machine learning or control theory.
• Problem Solver:
  You possess a strong theoretical background but are driven by practical application, with an ability to bridge the gap between RL theory and the constraints of physical, real-world systems.

• Competitive salary, bonus, 401(k) plan and equity in a rapidly growing startup
• Comprehensive health, dental, and vision coverage
• Opportunity to apply the latest AI technologies working with an experienced team