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Ai Reinforcement Learning Jobs in Seattle, WA (NOW HIRING)

YO AI Labs

AI Engineer - Remote

Seattle, WA · Remote

$80 - $120/hr

No prior AI experience is required. Key Responsibilities ... Create reinforcement learning environments for software engineering tasks. * Design tasks involving ...

YO AI Labs

AI Trainer - Remote

Seattle, WA · Remote

$80 - $120/hr

No prior AI experience is required. Key Responsibilities ... Create reinforcement learning environments for software engineering tasks. * Design tasks involving ...

YO AI Labs

AI Engineer - Remote

Seattle, WA · Remote

$80 - $120/hr

No prior AI experience is required. Key Responsibilities ... Create reinforcement learning environments for software engineering tasks. * Design tasks involving ...

YO AI Labs

AI Trainer - Remote

Seattle, WA · Remote

$80 - $120/hr

No prior AI experience is required. Key Responsibilities ... Create reinforcement learning environments for software engineering tasks. * Design tasks involving ...

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

Ai Reinforcement Learning information

See Seattle, WA salary details

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$46

$79

How much do ai reinforcement learning jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for ai reinforcement learning in Seattle, WA is $46.31, according to ZipRecruiter salary data. Most workers in this role earn between $33.65 and $60.19 per hour, depending on experience, location, and employer.

What is AI reinforcement learning?

AI reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, which it uses to improve its future performance. Reinforcement learning is widely used in applications such as robotics, game playing, recommendation systems, and autonomous vehicles. Unlike supervised learning, RL doesn't require labeled input/output pairs and learns through trial and error.

What are the key skills and qualifications needed to thrive as an AI reinforcement learning specialist?

To thrive as an AI Reinforcement Learning Specialist, you need strong expertise in machine learning, deep learning, and mathematics, usually backed by a degree in computer science, engineering, or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow or PyTorch, and experience with RL-specific libraries like OpenAI Gym are typically required. Analytical thinking, problem-solving abilities, and effective collaboration are essential soft skills for excelling in this role. These skills and qualifications are crucial for developing, optimizing, and deploying RL algorithms that solve complex, real-world problems.

What are some common challenges faced by AI reinforcement learning specialists when deploying models in real-world applications?

AI Reinforcement Learning (RL) specialists often encounter challenges such as ensuring the reliability and safety of RL agents outside of controlled environments. Real-world data can be noisy and unpredictable, making it difficult for models trained in simulations to generalize. Additionally, RL algorithms typically require significant computational resources and time for training, which can be a constraint in fast-paced projects. Collaboration with domain experts and software engineers is essential to adapt algorithms to production systems and continuously monitor performance for unexpected behaviors.

What is the difference between Ai Reinforcement Learning vs Data Scientist?

AspectAi Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of algorithmsDegree in Statistics, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, data analysis teams, consulting firms
Industry UsageAI product development, autonomous systems, roboticsBusiness insights, predictive modeling, data analysis
Common Search/ComparisonYesYes

Ai Reinforcement Learning focuses on developing algorithms that enable machines to learn through trial and error to make decisions. Data Scientists analyze data to extract insights and build predictive models. While both roles require programming skills and a background in data or algorithms, reinforcement learning specialists primarily work on AI systems that learn from interactions, whereas Data Scientists focus on interpreting data to inform business decisions.

What are popular job titles related to Ai Reinforcement Learning jobs in Seattle, WA?

For Ai Reinforcement Learning jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Ai Reinforcement Learning jobs in Seattle, WA look for?

The top searched job categories for Ai Reinforcement Learning jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Ai Reinforcement Learning jobs?

Cities near Seattle, WA with the most Ai Reinforcement Learning job openings:

Ai Reinforcement Learning jobs near you
Infographic showing various Ai Reinforcement Learning job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $96,332 per year, or $46.3 per hour.

AI Research Scientist, New Grad - Agents & Reinforcement Learning

Snowflake

Bellevue, WA • On-site

$100 - $120/hr

Other

Re-posted 19 days ago

Job description

We are hiring an AI Research Scientist (New Grad) for our AI Research team. Our team is pushing the frontier of autonomous, self‑improving AI systems — building agents that reason, code, and learn at scale inside the Snowflake Data Cloud. This role sits at the intersection of agentic AI and reinforcement learning, where your research will directly shape how enterprises leverage intelligent automation.

AS AN AI RESEARCH SCIENTIST AT SNOWFLAKE, YOU WILL:
  • Design and develop agentic frameworks powered by recursive self‑improvement loops, enabling AI systems that iteratively refine their own capabilities and strategies
  • Build and evaluate autonomous research agents — systems capable of autonomously formulating hypotheses, executing experiments, and synthesizing findings
  • Develop coding agents that understand, generate, and debug code across complex, multi‑step programming tasks
  • Conduct research in reinforcement learning with a focus on RLHF, DPO, and PPO as mechanisms for aligning and improving agentic behaviors
  • Contribute to multi‑agent systems where specialized agents collaborate, negotiate, and self‑organize to solve enterprise‑scale problems
  • Develop and curate training data pipelines — both synthetic and human‑annotated — to support novel agentic and RL research domains
  • Publish research findings at top‑tier venues such as NeurIPS, ICML, ICLR, and ACL
OUR IDEAL AI RESEARCH SCIENTIST WILL HAVE:
  • PhD in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field (completing or recently completed; or equivalent research experience)
  • Foundational expertise in reinforcement learning algorithms, including RLHF, DPO, PPO, or multi‑agent systems
  • Research experience in LLM post‑training, fine‑tuning, or reasoning model development
  • Demonstrated ability to implement and experiment with agentic architectures — including tool‑use, planning, and self‑correction loops
  • Proficiency in Python and at least one deep learning framework (PyTorch or JAX strongly preferred)
  • Strong mathematical and analytical foundation — comfortable working at the intersection of theory and empirical research
  • At least one first‑author or co‑authored publication or preprint in a relevant AI/ML area
BONUS POINTS FOR THE FOLLOWING:
  • Hands‑on experience building or evaluating coding agents or autonomous research agents
  • Familiarity with recursive self‑improvement frameworks or automated AI scientist paradigms
  • Experience with large‑scale distributed training or efficient training paradigms
  • Background in mathematical reasoning, structured decision‑making, or program synthesis
  • Exposure to domain‑specific AI applications in healthcare, finance, or enterprise workflows

Snowflake’s AI Research team operates at the frontier of what autonomous AI systems can do — not as a research lab disconnected from practice, but as a team where your work ships into a platform used by thousands of enterprises. You’ll be working alongside researchers and engineers building the next generation of agentic infrastructure, with access to large‑scale compute, real‑world data challenges, and the shortest possible path from research idea to product impact.

For a new grad role, this is a rare opportunity to grow your research career while contributing to systems that actually run in production — shaping how AI agents reason, learn, and improve themselves inside the world’s leading data cloud.

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

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