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Remote Reinforcement Learning Jobs in Washington

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

Familiarity or experience with model distillation, synthetic data generation, reinforcement ... Fully remote, U.S.-based * Health Benefits : Comprehensive health, dental, and vision coverage Time ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

Familiarity or experience with model distillation, synthetic data generation, reinforcement ... Fully remote, U.S.-based * Health Benefits : Comprehensive health, dental, and vision coverage Time ...

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 ...

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Humana

Lead Data Scientist

Washington, DC · On-site +1

Experience with reinforcement learning * Experience with large-scale ETL Preferred Qualifications ... per week remote/home. Office Location Options: * Louisville, KY * Boston, MA * New York, NY

Helsing

Software Engineer, Rust

Washington, DC · On-site

... 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 JobsRemote Reinforcement Learning JobsRemote Reinforcement Learning Jobs in Washington

Remote Reinforcement Learning information

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.
What are the most commonly searched types of Reinforcement Learning jobs in Washington? The most popular types of Reinforcement Learning jobs in Washington are:
What are popular job titles related to Remote Reinforcement Learning jobs in Washington? For Remote Reinforcement Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Remote Reinforcement Learning jobs in Washington look for? The top searched job categories for Remote Reinforcement Learning jobs in Washington are:
What cities in Washington are hiring for Remote Reinforcement Learning jobs? Cities in Washington with the most Remote Reinforcement Learning job openings:
Remote Reinforcement Learning jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site, Remote

$130K - $200K/yr

Other

Medical, Dental, Vision, PTO

Posted 22 days ago

Job description

About the Role:

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications.

This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI.

You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies.

Responsibilities may include:

  • Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains.
  • Develop and improve classification systems for safety, security, abuse detection, and intelligence applications.
  • Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems.
  • Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability.
  • Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety.
  • Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation.
  • Own ML projects from initial research and prototyping through production deployment and monitoring.
  • Partner with software engineers to productionize ML systems and support ongoing improvements.
  • Provide technical expertise and guidance across client engagements and internal research initiatives.

We're looking for someone who: 

  • Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration; 
  • Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion;
  • Communicates technical concepts clearly to both technical and non-technical audiences;
  • Is resourceful, proactive, and comfortable operating in a fast-moving startup environment.
  • Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security.

Requirements:

  • 3-5+ years of professional experience building and deploying machine learning systems.
  • Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
  • Experience working across multiple modalities, with expertise in one or more of:
    • Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas.
    • Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas.
  • Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
  • Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
  • Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
  • Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.

Preferred:

Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.

Compensation:

  • 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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