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Remote Reinforcement Learning Jobs in Washington, DC

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

You will work on problems involving reinforcement learning, model evaluations, language models ... Fully remote, U.S.-based * Health Benefits : Comprehensive health, dental, and vision coverage

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

... * We're remote - Work from wherever you want. We collaborate in real time on Slack or ... Experience with reinforcement learning algorithms and applications * Digital signal processing ...

Booz Allen Hamilton, Inc.

Autonomy SME, Lead

Washington, DC · On-site +1

$116K - $152K/yr

Develop reinforcement learning and autonomy algorithms for navigation and mission execution ... Remote : If this position is listed as remote, there may still be occasions when you are required ...

Maximus

Sr Specialist, AI & Technical Upskilling

VA · On-site +1

... Remote Country United States Working time Full-time Description & Requirements Maximus' Learning ... assets, practical reinforcement, and measurable outcomes. This role is ideal for a learning ...

Helsing

Software Engineer, Rust

Washington, DC · On-site +1

... reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The ... remote and/or hybrid work available depending on position. All compensation and benefits are ...

Helsing

Software Engineer, Rust

Washington, DC · On-site +1

... reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The ... remote and/or hybrid work available depending on position. All compensation and benefits are ...

All JobsReinforcement Learning JobsRemote Reinforcement Learning JobsRemote Reinforcement Learning Jobs in Washington, DC

Remote Reinforcement Learning information

See Washington, DC salary details

$12.5K

$95K

$158.6K

How much do remote reinforcement learning jobs pay per year?

As of Aug 22, 2026, the average yearly pay for remote reinforcement learning in Washington, DC is $95,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $157,400.00 per year, depending on experience, location, and employer.

What is a remote reinforcement learning?

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?

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 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.

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 the most commonly searched types of Reinforcement Learning jobs in Washington, DC?

The most popular types of Reinforcement Learning jobs in Washington, DC are:

What are popular job titles related to Remote Reinforcement Learning jobs in Washington, DC?

For Remote Reinforcement Learning jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Remote Reinforcement Learning jobs in Washington, DC look for?

The top searched job categories for Remote Reinforcement Learning jobs in Washington, DC are:

Remote Reinforcement Learning jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site, Remote

$130K - $200K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 19 days ago

Job description

About the Role

We are seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems across AI safety and model evaluation applications.

This role combines strong ML engineering with an experimental mindset. You will work on problems involving reinforcement learning, model evaluations, language models, multimodal systems, and classifiers, taking ambiguous technical questions and turning them into rigorous experiments and scalable systems.

You will collaborate closely with engineers, analysts, red teamers, and subject-matter experts supporting leading AI organizations.

What You'll Do
  • Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems.
  • Develop and evaluate models across reinforcement learning, NLP/LLMs, computer vision, and multimodal ML.
  • Build evaluation pipelines, benchmarks, datasets, and metrics for frontier AI systems.
  • Train, fine-tune, and evaluate models for safety, security, and other high-impact applications.
  • Develop reliable tooling and infrastructure to run ML experiments and evaluations at scale.
  • Analyze results, identify model failure modes, and translate findings into new experiments and technical approaches.
What We're Looking For
  • 3-5+ years of experience in machine learning, research engineering, or a related technical field.
  • Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
  • Hands-on experience training, fine-tuning, or evaluating modern ML models.
  • Strong understanding of experimental design, model evaluation, and quantitative analysis.
  • Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents.
  • Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
  • Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems.
Nice to Have
  • Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques.
  • Experience evaluating frontier language or multimodal models.
  • Experience with adversarial evaluations, robustness testing, or AI safety.
  • Experience with distributed training, cloud ML infrastructure, or large-scale ML systems.

We don't expect candidates to have experience across every area above. We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques.

Compensation & Benefits
  • Salary Range: $130K-$200K, depending on experience and location
  • Bonus: Performance-based annual bonus
  • Professional Development: Support for conferences, continuing education, or leadership training
  • Work Environment: Fully remote, U.S.-based
  • Health Benefits: Comprehensive health, dental, and vision coverage
  • Time Off: Generous PTO and paid holiday schedule

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