ZipRecruiter

Log In

1

Reinforcement Learning Game Jobs (NOW HIRING)

next page

Showing results 1-20

All JobsReinforcement Learning Game Jobs

Reinforcement Learning Game information

See salary details

$7

$20

$37

How much do reinforcement learning game jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for reinforcement learning game in the United States is $20.94, according to ZipRecruiter salary data. Most workers in this role earn between $14.18 and $23.80 per hour, depending on experience, location, and employer.

What is a reinforcement learning game?

A Reinforcement Learning (RL) Game is a simulation or environment designed for testing and training artificial intelligence (AI) agents using reinforcement learning techniques. In these games, an agent interacts with the environment by taking actions and receiving rewards based on its performance, allowing it to learn optimal strategies over time. RL games are widely used in research to benchmark algorithms and in industry to develop intelligent behaviors for robots, automated systems, or video game characters. Popular RL games include OpenAI Gym environments, Atari games, and custom simulations built for specific tasks.

What are the key skills and qualifications needed to thrive as a reinforcement learning engineer in the gaming industry?

To thrive as a Reinforcement Learning Engineer in game development, you need a strong background in machine learning, algorithms, and programming (typically Python), often supported by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries (such as OpenAI Gym or Unity ML-Agents), as well as experience with simulation environments, is typically required. Critical thinking, creativity, and effective communication help you design innovative AI solutions and collaborate with interdisciplinary teams. These skills are crucial for developing intelligent game agents that enhance player experience and drive technological advancement in interactive entertainment.

How does a reinforcement learning game engineer typically collaborate with game designers and data scientists during development?

As a Reinforcement Learning Game Engineer, you will frequently collaborate with game designers to integrate RL agents in ways that enhance gameplay and balance. Close coordination with data scientists is also common, as they help analyze agent behaviors and performance data to refine training environments and reward structures. Regular cross-functional meetings and iterative testing sessions are standard, ensuring that RL-driven features both align with the game's vision and deliver measurable improvements. This collaborative environment fosters innovation and provides valuable insights into both AI and game design best practices.

What other helpful pages are available for Reinforcement Learning Game?

Other pages related to Reinforcement Learning Game:

Infographic showing various Reinforcement Learning Game job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $43,561 per year, or $20.9 per hour.

Reinforcement Learning Environment Engineer (Contract)

Alameda, CA • On-site

Cobalt
See more jobs at Cobaltexternal

Other

Posted 10 days ago

Key responsibilities

  • Design and build task environments with programmatic success criteria, including multi-step and tool-using tasks.

  • Specify reward functions and partial-credit schemes, and stress-test them for exploitation by capable agents.

  • Evaluate agent trajectories, identify failure points, and classify failures into a consistent taxonomy.

Job description

About the role:

Cobalt is seeking people who can build the environments frontier labs train and evaluate agents in: tasks with real difficulty, unambiguous success conditions, and scoring that survives contact with a capable model.

This opportunity is suited to reinforcement learning researchers, research engineers, simulation and tooling engineers, and people who have built serious benchmarks, competition problems, or training environments. Depth in RL is valuable, but so is the engineering discipline required to make an environment reproducible and hard to game.

You do not need prior experience in data annotation. What matters is that you can take a domain, decide what a meaningful task in it looks like, and build something that measures it correctly.


What you'll do:

Depending on the project, you may:

  • Design and build task environments with programmatic success criteria, including multi-step and tool-using tasks that cannot be solved by a shortcut
  • Specify reward functions and partial-credit schemes, and stress-test them for the ways a capable agent would exploit them rather than solve the task
  • Produce written reasoning traces and reference solutions showing how a competent human works through the tasks you build
  • Evaluate agent trajectories, identifying the specific step at which behavior goes wrong, and classifying failures into a consistent taxonomy
  • Assess whether a scored result reflects genuine task completion, and flag cases where the environment or the metric is measuring the wrong thing

Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.


Required qualifications:

  • Direct experience with reinforcement learning, agent evaluation, simulation, or benchmark and environment construction, whether in research, industry, or substantial open-source work
  • Strong software engineering ability in Python, sufficient to build reproducible environments, harnesses, and automated scoring
  • A PhD in a quantitative discipline, or equivalent depth demonstrated through published work, open-source contributions, or production systems
  • Understanding of reward hacking and specification gaming, and the instinct to look for them in your own designs before someone else does
  • Ability to explain each step of your reasoning and design decisions clearly in writing


Why join Cobalt AI:

  • Advance frontier AI where it counts. Apply your expertise to data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
  • Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while developing a working understanding of how frontier models are trained and assessed.
  • Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
  • Set your own schedule. Flexible 10 to 40 hour weeks that fit around your existing work and your life.
  • Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.

Apply