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Postdoctoral In Reinforcement Learning Jobs (NOW HIRING)

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

Stay up-to-date with the latest research and advancements in reinforcement learning. Preferred Qualifications * BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

Advanced Micro Devices

$150 - $190/hr

You are fluent in both RL theory and the practical path from ablation to production-scale training ... Strong publication record in reinforcement learning or closely related machine learning areas.

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How much do postdoctoral in reinforcement learning jobs pay per year?

As of Aug 22, 2026, the average yearly pay for postdoctoral in reinforcement learning in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

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Infographic showing various Postdoctoral In Reinforcement Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

AI Research Scientist, New Grad - Agents & Reinforcement Learning

Snowflake

Bellevue, WA • On-site

$100 - $120/hr

Other

Re-posted 5 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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