Hammerhead AI

Reinforcement Learning Engineer

Redwood City

Sponsorship not specifiedDetected 216 days ago

PythonAlgorithmsMachine LearningPyTorchRoboticsResearchCollaboration

About the role

As a Reinforcement Learning Engineer, you will be the architect of the core intelligence for Hammerhead's ORCA platform.
This role is for a hands-on expert who is passionate about applying cutting-edge RL research to complex, real-world industrial systems.

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.
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.
Build and train RL agents that can generalize to real-world, physical systems.
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.
🤝 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

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.
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.
3+ years of experience applying RL to real-world problems, preferably in industrial automation, robotics, autonomous vehicles, energy systems, or other physical systems.
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.

Technical Skills: Deep proficiency in Python and modern ML frameworks such as PyTorch, Jax, or TensorFlow.

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.
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
Join our team to shape the foundation of tomorrow's AI infrastructure
Visit our Careers page at (hammerheadco dot ai / careers) to apply

💰 Receive competitive compensation, equity, and benefits in a high-growth, mission-driven environment.

💰 Receive competitive compensation, equity, and benefits in a high-growth, mission-driven environment.
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.

This listing is sourced directly from Hammerhead AI's careers page and normalized into a canonical job model.