ZipRecruiter

Log In

1

Deep Reinforcement Learning Jobs (NOW HIRING)

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.

previous page

Showing results 41-60

All JobsTechnology JobsDeep Reinforcement Learning Jobs

Deep Reinforcement Learning information

See salary details

$28.5K

$58.3K

$80K

How much do deep reinforcement learning jobs pay per year?

As of Aug 18, 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 is deep reinforcement learning?

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 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 are the key skills and qualifications needed to thrive in deep reinforcement learning?

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 August 2026, with employment types broken down into 73% Full Time, 16% Part Time, and 11% Contract. Highlights an 84% In-person, and 16% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site

$200K - $400K/yr

Full-time

Re-posted 10 days ago

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.
Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.
This role focuses on applying and advancing reinforcement learning across simulation and real-world environments-improving policy performance, robustness, and long-horizon decision-making in embodied systems.
Responsibilities
  • Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments
  • Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks
  • Develop reward modeling, credit assignment, and exploration strategies for complex, long-horizon behaviors
  • Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
  • Work across online and offline RL settings, including learning from large-scale logged robot data
  • Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
  • Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
  • Design evaluation frameworks to measure policy performance, stability, and generalization
Requirements
  • Experience developing and applying reinforcement learning algorithms in complex environments
  • Strong understanding of RL fundamentals (e.g., policy optimization, value methods, model-based RL)
  • Experience training policies in simulation and/or real-world systems
  • Proficiency in Python and deep learning frameworks such as PyTorch
  • Experience with large-scale experimentation and distributed training systems
  • Strong experimental rigor and ability to diagnose and improve learning systems
  • Solid software engineering skills and ability to build scalable, reliable systems
  • Ability to operate independently and drive ambiguous, high-impact technical problems
Bonus Qualifications
  • Experience applying RL to robotics, control systems, or embodied AI
  • Experience with large-scale RL infrastructure (distributed rollouts, simulation at scale)
  • Background in offline RL, imitation learning, or hybrid learning approaches
  • Experience with reward modeling or human-in-the-loop learning
  • Experience at leading AI labs such as OpenAI, Google DeepMind, Anthropic, or xAI
  • Familiarity with robotics systems, simulation environments, or real-world deployment constraints
  • Publication record in reinforcement learning, machine learning, or robotics

The US base salary range for this full-time position is between $200,000 - $400,000
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

Apply