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

Learn more about Hammerhead

These AutoGrid alums want to change how data centers use power

How Hammerhead Wants to Rewrite the Economics of AI

News & Blogs

Role Description

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.

Key Responsibilities

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.

Qualifications

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 TensorFlow. 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.

What We Offer

Competitive salary, bonus, 401(k) plan and equity in a rapidly growing startup