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Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

MLabs

Reinforcement Learning Engineer

New York, NY

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site | Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

Bostondynamics

Staff, ML Research Scientist

Waltham, MA · On-site

$154K - $192K/yr

Experience in the full modeling cycle from research to deployment of modern Deep Learning architectures such as Transformers, VLMs/VLAs, and Deep Reinforcement Learning. * Knowledge of ...

poolside

Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

What unites us is our deep care for what we build together. We're in a race that requires hard work ... ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ...

Boston Dynamics

Staff, ML Research Scientist

Waltham, MA · On-site

$154K - $192K/yr

Experience in the full modeling cycle from research to deployment of modern Deep Learning architectures such as Transformers, VLMs/VLAs, and Deep Reinforcement Learning. * Knowledge of ...

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

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Deep Reinforcement Learning information

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How much do deep reinforcement learning jobs pay per year?

As of Jun 17, 2026, the average yearly pay for deep reinforcement learning in the United States is $58,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,000.00 per year, depending on experience, location, and employer.

What does a typical day look like for someone working in Deep Reinforcement Learning?

A typical day for a Deep Reinforcement Learning professional involves designing algorithms, running experiments, analyzing results, and optimizing models to improve performance. You may collaborate regularly with data scientists, software engineers, and domain experts to integrate RL solutions into larger systems or products. Tasks often include reading the latest research, contributing to code reviews, and documenting findings while troubleshooting technical challenges. This dynamic environment encourages continuous learning and teamwork, ensuring you stay at the forefront of AI innovation.

What is a Deep Reinforcement Learning job?

A Deep Reinforcement Learning (DRL) job involves researching, developing, and applying AI models that use reinforcement learning techniques combined with deep learning. Professionals in this role design algorithms that enable agents to learn optimal decision-making policies through trial and error. Common applications include robotics, game AI, autonomous systems, and financial modeling. This job typically requires expertise in machine learning, neural networks, and programming languages like Python, along with frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in the Deep Reinforcement Learning position, and why are they important?

To thrive in Deep Reinforcement Learning, you need expertise in machine learning, programming (Python, TensorFlow, or PyTorch), and applied mathematics, often supported by an advanced degree in computer science or a related field. Familiarity with version control systems, cloud computing platforms, and relevant certifications in AI or data science are valuable assets. Strong problem-solving abilities, collaboration, and effective communication are important soft skills in this position. These skills are essential for developing, implementing, and iterating cutting-edge algorithms that solve complex real-world problems in dynamic environments.

More about Deep Reinforcement Learning jobs
What are the most commonly searched types of Deep Reinforcement Learning jobs? The most popular types of Deep Reinforcement Learning jobs are:
What states have the most Deep Reinforcement Learning jobs? States with the most job openings for Deep Reinforcement Learning jobs include:
What job categories do people searching Deep Reinforcement Learning jobs look for? The top searched job categories for Deep Reinforcement Learning jobs are:
Deep Reinforcement Learning jobs near you
Infographic showing various Deep Reinforcement Learning job openings in the United States as of June 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.

Reinforcement Learning Engineer

HammerheadAI

Redwood City, CA

Other

Medical, Dental, Vision, Retirement

Posted 2 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