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Full Time Reinforcement Learning Jobs (NOW HIRING)

Senior Machine Learning Engineer

$125K - $165K/yr

Familiarity with reinforcement learning or bandit models Nice to Have * Experience with Java and ... For full-time positions: * Competitive salary packages * Equity * Home office stipend

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... for full-time employees: * 401(k) Plan: Includes a 6% company match. * Equity: Company stock ...

Strong background in one or more of the following: reinforcement learning, causal inference, LLM ... This fulltime position is eligible for a comprehensive benefits package designed to support the ...

Staff AI Research Engineer

Salem, OR · On-site +1

$216K - $338K/yr

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... for full-time employees: * 401(k) Plan: Includes a 6% company match. * Equity: Company stock ...

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Full Time Reinforcement Learning information

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$21K

$61.7K

$114.5K

How much do full time reinforcement learning jobs pay per year?

As of Jul 23, 2026, the average yearly pay for full time reinforcement learning in the United States is $61,692.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,000.00 and $72,000.00 per year, depending on experience, location, and employer.

What is the difference between Full Time Reinforcement Learning vs Data Scientist?

AspectFull Time Reinforcement LearningData Scientist
Required CredentialsAdvanced degree in CS, ML, or related field; experience with RL frameworksDegree in CS, Statistics, or related; strong programming and analytical skills
Work EnvironmentResearch labs, AI companies, tech firms focusing on RL projectsBusiness, tech companies, consulting firms analyzing data for insights
Industry UsageAI research, robotics, gaming, autonomous systemsFinance, healthcare, marketing, e-commerce, and more

Full Time Reinforcement Learning specialists focus on developing RL algorithms and models, often in research or AI product development. Data Scientists analyze data to extract insights and support decision-making across various industries. While both roles require strong technical skills, RL roles are more specialized in AI research and development, whereas Data Scientists have broader applications in data analysis and business strategy.

More about Full Time Reinforcement Learning jobs
What cities are hiring for Full Time Reinforcement Learning jobs? Cities with the most Full Time Reinforcement Learning job openings:
What are the most commonly searched types of Reinforcement Learning jobs? The most popular types of Reinforcement Learning jobs are:
Infographic showing various Full Time Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $61,692 per year, or $29.7 per hour.
AI / Machine Learning Modeler / Engineer - National Capital Region (NCR); Must have an active TS/...

AI / Machine Learning Modeler / Engineer - National Capital Region (NCR); Must have an active TS/...

Synertex LLC

Bethesda, MD

$58.50 - $75.75/hr

Full-time

Posted 24 days ago


Job description

AI / Machine Learning Modeler / Engineer 

 Bethesda, MD | McLean, VA | Chantilly, VA
Full-Time | On-site | Position Contingent Upon Award

Join a mission that matters. We are seeking a skilled AI / Machine Learning Modeler / Engineer to support critical government and intelligence programs by developing, deploying, and maintaining advanced machine learning models in production environments. This role focuses on building high-performing, scalable AI solutions using supervised, unsupervised, and reinforcement learning techniques to solve complex operational challenges.

Position Overview: 

Develops, deploys, and maintains AI and machine learning models in support of critical government and intelligence missions. This role applies advanced machine learning techniques to build, evaluate, and sustain models that operate in production environments with high performance, reliability, and scalability requirements.

 RESPONSIBILITIES:
  • Develop, deploy, and maintain AI and machine learning models using provided datasets and customer requirements.
  • Test and develop machine learning models to meet or exceed defined performance evaluation metrics.
  • Apply concepts of supervised, unsupervised, and reinforcement learning in model development.
  • Perform hyperparameter tuning and model evaluation to optimize performance.
  • Monitor machine learning models in production environments to ensure reliability, efficiency, and scalability.
REQUIREMENTS:TS/SCI clearance with polygraph required, including additional security screenings
Education and Experience Requirements

Candidates must meet the following minimum education and relevant experience combinations by level:

Entry Level:
  • HS/GED + 6 years experience
  • Associate's + 4 years experience
  • Bachelor's + 2 years experience
  • Master's degree (experience may be substituted as appropriate)
Journeyman Level:
  • HS/GED + 8 years experience
  • Associate's + 6 years experience
  • Bachelor's + 4 years experience
  • Master's + 2 years experience
Senior Level:
  • HS/GED + 10 years experience
  • Associate's + 8 years experience
  • Bachelor's + 6 years experience
  • Master's + 4 years experience
  • PhD + 2 years experience
Expert / Master Level:
  • HS/GED + 12 years experience
  • Associate's + 10 years experience
  • Bachelor's + 8 years experience
  • Master's + 6 years experience
  • PhD + 4 years experience
 
 

Join a mission-driven team advancing government communication capabilities and operational readiness. Apply today and become part of Synertex LLC's legacy of innovation, leadership, and excellence.


Synertex logo

About Synertex

Sourced by ZipRecruiter

Industry

Guided missile and space vehicle manufacturing

Company size

11 - 50 Employees

Headquarters location

McLean, VA, US

Year founded

2017