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Remote Reinforcement Learning Jobs in Seattle, WA

DocuSign

Senior Machine Learning Engineer

Seattle, WA · On-site +1

$186K - $300K/yr

We are building a self-healing ecosystem where Multi-Agent Systems and Reinforcement Learning (RL ... Employee divides their time between in-office and remote work. Access to an office location is ...

DocuSign

Senior Machine Learning Engineer

Seattle, WA · On-site +1

$186K - $300K/yr

We are building a self-healing ecosystem where Multi-Agent Systems and Reinforcement Learning (RL ... Employee divides their time between in-office and remote work. Access to an office location is ...

Phaidra

Expression of Interest - General

Seattle, WA · Remote

We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data ... We are a 100% remote company. * Competitive compensation & meaningful equity. * Outsized ...

Phaidra

Expression of Interest - Engineering

Seattle, WA · Remote

$19 - $24.75/hr

We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data ... We are a 100% remote company. * Competitive compensation & meaningful equity. * Outsized ...

Phaidra

Senior Software Engineer (Data Platform)

Seattle, WA · Remote

$139K - $183K/yr

We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data ... Ability to collaborate and communicate effectively in an all-remote setting. * Approach your work ...

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All JobsRemote Reinforcement Learning JobsRemote Reinforcement Learning Jobs in Seattle, WA

Remote Reinforcement Learning information

See Seattle, WA salary details

$12.5K

$95.5K

$159.3K

How much do remote reinforcement learning jobs pay per year?

As of Jun 12, 2026, the average yearly pay for remote reinforcement learning in Seattle, WA is $95,464.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,900.00 and $158,200.00 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.
What are popular job titles related to Remote Reinforcement Learning jobs in Seattle, WA? For Remote Reinforcement Learning jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Remote Reinforcement Learning jobs in Seattle, WA look for? The top searched job categories for Remote Reinforcement Learning jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Remote Reinforcement Learning jobs? Cities near Seattle, WA with the most Remote Reinforcement Learning job openings:
Remote Reinforcement Learning jobs near you
ML Scientist (Pricing Reinforcement Learning) | REMOTE |

ML Scientist (Pricing Reinforcement Learning) | REMOTE |

TriOptus LLC

Bellevue, WA • On-site, Remote

Contractor

Posted 9 days ago

Job description

100% telecommute
Description: We seek a Senior ML Scientist to drive innovation in AI MLbased dynamic pricing algorithms and personalized offer experiences This role will focus on designing and implementing advanced machine learning models including reinforcement learning techniques like Contextual Bandits Qlearning SARSA and more By leveraging algorithmic expertise in classical ML and statistical methods you will develop solutions that optimize pricing strategies improve customer value and drive measurable business impact
Responsibilities:
  • Algorithm Development - Conceptualize design and implement state-of-the-art ML models for dynamic pricing and personalized recommendations
  • Reinforcement Learning Expertise - Develop and apply RL techniques including Contextual Bandits Qlearning SARSA and concepts like Thompson Sampling and Bayesian Optimization to solve pricing and optimization challenges
  • AI Agents for Pricing - Build AIdriven pricing agents that incorporate consumer behaviour demand elasticity and competitive insights to optimize revenue and conversion
  • Rapid ML Prototyping - Experience in quickly building testing and iterating on ML prototypes to validate ideas and refine algorithms
  • Feature Engineering - Engineer large-scale consumer behavioural feature stores to support ML models ensuring scalability and performance
  • CrossFunctional Collaboration - Work closely with Marketing Product and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact
  • Controlled Experiments - Design analyze and troubleshoot AB and multivariate tests to validate the effectiveness of your models

Qualifications:
  • 8 years in machine learning
  • 5 years in reinforcement learning recommendation systems pricing algorithms pattern recognition or artificial intelligence
  • Expertise in classical ML techniques eg Classification Clustering Regression using algorithms like XGBoost Random Forest SVM and KMeans with handson experience in RL methods such as Contextual Bandits Qlearning SARSA and Bayesian approaches for pricing optimization
  • Proficiency in handling tabular data including sparsity cardinality analysis standardization and encoding
  • Proficient in Python and SQL including Window Functions Group By Joins and Partitioning
  • Experience with ML frameworks and libraries such as scikitlearn TensorFlow and PyTorch
  • Knowledge of controlled experimentation techniques including causal AB testing and multivariate testing
  • 5+ Yrs Expereince in Pricing Reinforcement Learning
  • 8+ Yrs Experience in Machine Learning
  • Expert in Python & Tabular Data
  • SQL
  • Knowledge of AB Testing

Required Skills : Machine Learning
Basic Qualification :
Additional Skills : ML Developer
Background Check : No
Drug Screen : No

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