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Remote Reinforcement Learning Jobs (NOW HIRING)

poolside

Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving ... Fully remote work & flexible hours * 37 days/year of vacation & holidays * Health insurance ...

Centific

Applied Reinforcement Learning Engineer

Palo Alto, CA or Seattle, WA (Hybrid/Remote) About the Team Centific AI Research advances foundational AI models and applications through reinforcement learning, alignment, and human-centered ...

Statheros

Develop and train reinforcement learning models for real-world applications, focusing on efficiency ... Remote work location. * Competitive salary. * Flexible work schedule. * Opportunities for ...

Motional

Senior Machine Learning Engineer, Data Mining

$125K - $165K/yr

Reinforcement Learning for Data Discover : Build RL-based policy learning and reasoning systems for ... be fully remote. The salary range for this role is an estimate based on a wide range of ...

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$11K

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How much do remote reinforcement learning jobs pay per year?

As of Jul 3, 2026, the average yearly pay for remote reinforcement learning in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

What is a Remote Reinforcement Learning job?

A Remote Reinforcement Learning job involves developing and applying reinforcement learning algorithms while working from a location outside of a traditional office environment. Professionals in this field focus on creating systems where agents learn optimal behaviors through trial and error, often using feedback from their environment. These jobs typically require expertise in machine learning, programming, and mathematics, and are commonly found in industries like robotics, gaming, and autonomous systems. Working remotely allows researchers and engineers to collaborate with global teams using digital tools and platforms.

What are the key skills and qualifications needed to thrive as a Remote Reinforcement Learning Engineer, and why are they important?

To thrive as a Remote Reinforcement Learning Engineer, you need a strong background in machine learning, statistics, and programming (especially Python), often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and RL-specific libraries like OpenAI Gym, along with experience using cloud computing platforms, is typically required. Excellent problem-solving skills, self-motivation, and effective remote communication help individuals excel in distributed teams. These skills ensure the successful design, implementation, and deployment of reinforcement learning solutions while collaborating efficiently in a remote work environment.

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

AspectRemote Reinforcement Learning
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; knowledge of RL algorithms
Work EnvironmentResearch-focused, experimental, often involves simulation and algorithm development
Employer & Industry UsageTech companies, research labs, AI startups focusing on autonomous systems
Common Search & Comparison IntentUnderstanding specialized AI roles, research focus, and technical skills

Remote Reinforcement Learning specialists focus on developing algorithms that enable machines to learn through trial and error in simulated or real environments. In contrast, Remote Machine Learning Engineers typically work on deploying and optimizing various machine learning models across applications. While both roles require strong programming skills and knowledge of AI, reinforcement learning emphasizes decision-making processes, whereas machine learning engineering covers a broader range of models and deployment strategies.

What are common challenges faced when working remotely in a Reinforcement Learning role and how can they be addressed?

Working remotely in a Reinforcement Learning role often involves overcoming communication barriers with cross-functional teams, managing large-scale experiments without on-site resources, and staying updated with rapidly evolving research. To address these challenges, it's important to establish regular check-ins with colleagues, utilize cloud-based platforms for experiment management, and participate in virtual seminars or journal clubs. Developing strong self-motivation and time management skills is also crucial to maintain productivity in a remote environment.
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Remote Reinforcement Learning jobs near you

Member of Engineering (Reinforcement Learning)

poolside

Remote

$99K - $136K/yr

Full-time

Medical, PTO

Posted 6 days ago

Job description

ABOUT POOLSIDE
In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.
Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.
ABOUT OUR TEAM
We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.
Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We're in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we've assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.
ABOUT THE ROLE
You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. This is a hands-on role where you'll work end-to-end from researching new exploration or training algorithms, to designing and scaling up RL environments, to implementing your ideas across the stack. You will have access to thousands of GPUs in this team.
YOUR MISSION
To push the frontier of reasoning and coding capabilities of foundational models, via Reinforcement Learning.
RESPONSIBILITIES
  • Research and experiment on ways to improve reasoning and code generation for LLMs. Own the full experiment life cycle from idea to experimentation and integration
  • Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Translate research ideas into clean, reusable codebases that other researchers can build on
  • Design, analyze, and iterate on data generation and training of LLMs
  • Implement and iterate on RL training pipelines that scale reliably across domains
  • Diagnose training instabilities and failures, debug RL runs and propose mitigation methods
  • Write high-quality, reproducible and maintainable code
SKILLS & EXPERIENCE
  • Experience with Large Language Models (LLM), including:
    • Understanding of the Transformer architecture and scaling laws
    • Mid-training and post-training techniques
    • Experience training reasoning and/or agentic models
    • Hands-on use of LLMs, with a sense of their capabilities and limitations
  • Reinforcement Learning experience
    • Solid grasp of Reinforcement Learning concepts and familiarity with modern algorithms
    • Experience developing distributed, large-scale RL pipelines from data creation to evaluations
  • Research experience
    • Scientific publications in any of the following topics: Reinforcement Learning, LLMs and reasoning models
    • Ability to discuss the latest research with sufficient level of detail
    • Is reasonably opinionated
  • Engineering skills
    • Strong machine learning, algorithm skills and engineering background
    • Experience with distributed training
    • Excellent programming skills in Python
    • Familiarity with a deep learning framework (Pytorch or JAX)

PROCESS
  • Intro call with one of our Founding Engineers
  • Technical Interview(s) with one of our Founding Engineers
  • Team fit call with the People team
  • Final interview with one of our Founding Engineers
BENEFITS
  • Fully remote work & flexible hours
  • 37 days/year of vacation & holidays
  • Health insurance allowance for you & dependents
  • 16 weeks of flexible, full-pay parental leave
  • Well-being, always-be-learning & home office allowances
  • Company-provided equipment
  • Frequent team get togethers
  • Diverse & inclusive people-first culture

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