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Internship Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques. * A strong ...

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 ...

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques. * A strong ...

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 ...

Traversal

AI Researcher

Manhattan, NY · On-site

As an AI Researcher, you will train intelligent AI agents for root cause analysis in high-pressure environments, leading research in areas like LLM fine-tuning and deep reinforcement learning.

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How much do internship deep reinforcement learning jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for internship deep reinforcement learning in the United States is $17.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What is an internship in deep reinforcement learning?

An internship in Deep Reinforcement Learning (DRL) is a temporary, hands-on position where interns learn and apply state-of-the-art machine learning algorithms that enable computers to learn decision-making tasks through trial and error. Interns typically work on projects involving neural networks, reward systems, and environments like games or simulations. These internships provide valuable experience with frameworks such as TensorFlow or PyTorch, and exposure to current research in artificial intelligence. The experience helps students or recent graduates build technical skills and prepare for careers in AI research or industry.

What types of projects or tasks can I expect to work on during a deep reinforcement learning internship?

As a Deep Reinforcement Learning (DRL) intern, you'll typically work on projects involving the development, implementation, and evaluation of reinforcement learning algorithms. This might include tasks like training agents in simulated environments, tuning hyperparameters, analyzing performance metrics, and collaborating with team members to integrate DRL solutions into larger systems. You'll also likely spend time reading recent research papers, experimenting with frameworks such as TensorFlow or PyTorch, and presenting your findings to the research team. Collaboration with mentors and other interns is common, and you'll gain hands-on experience that prepares you for more advanced roles in AI research or engineering.

What are the key skills and qualifications needed to thrive as an intern in deep reinforcement learning?

To thrive as an Intern in Deep Reinforcement Learning, you need a solid background in mathematics (especially linear algebra, probability, and calculus), programming (Python), and foundational knowledge in machine learning principles, usually supported by ongoing or completed coursework in computer science or related fields. Familiarity with frameworks and tools such as TensorFlow, PyTorch, OpenAI Gym, and experience using version control systems like Git are typically required. Analytical thinking, curiosity, and effective communication are essential soft skills for collaborating on research problems and sharing complex findings. These skills and qualities are crucial for contributing to innovative projects and successfully navigating the challenges of cutting-edge AI research.

What is the difference between Internship Deep Reinforcement Learning vs Data Science Intern?

AspectInternship Deep Reinforcement LearningData Science Intern
Required SkillsMachine learning, programming (Python), reinforcement learning conceptsStatistics, data analysis, programming (Python/R), data visualization
Work EnvironmentResearch labs, AI companies, tech startupsBusiness analytics, tech firms, consulting agencies
Industry UsageAI research, robotics, autonomous systemsBusiness intelligence, marketing, finance

Internship Deep Reinforcement Learning focuses on developing algorithms that enable systems to learn through trial and error, often in AI research or robotics. Data Science Internships involve analyzing data to extract insights and support decision-making. While both roles require programming skills, reinforcement learning emphasizes AI-specific techniques, whereas data science centers on statistical analysis and data visualization.

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Internship Deep Reinforcement Learning jobs near you
Infographic showing various Internship Deep Reinforcement Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.

Reinforcement Learning Engineer

Hammerhead AI

Redwood City, CA • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 7 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