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Reinforcement Learning Engineer Salary information

What is the difference between Reinforcement Learning Engineer Salary vs Machine Learning Engineer Salary?

Reinforcement Learning Engineer SalaryMachine Learning Engineer Salary
Average salary varies based on experience, location, and industry, typically ranging from $100,000 to $150,000 annually.Average salary also varies widely, generally between $90,000 and $140,000 annually, depending on similar factors.

Both roles require strong programming skills, knowledge of machine learning frameworks, and experience with data analysis. Reinforcement Learning Engineers focus specifically on developing algorithms for decision-making tasks, while Machine Learning Engineers work on broader AI models. Salaries are comparable, with Reinforcement Learning Engineers often earning slightly more in specialized industries.

What is the salary of reinforcement learning engineer?

The salary of a reinforcement learning engineer typically ranges from $100,000 to $160,000 annually, depending on experience, location, and industry. Senior roles or those in high-demand areas may offer higher compensation, often including benefits such as stock options and bonuses.

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Cities in California with the most Reinforcement Learning Engineer Salary job openings:

Reinforcement Learning Engineer Salary jobs near you

Reinforcement Learning Engineer

HammerheadAI

Redwood City, CA • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 21 days ago

Job description

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
  • 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

Apply