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Postdoctoral In Reinforcement Learning Jobs in California

JazzX AI

Senior Staff AI Engineer

Los Altos, CA

$65.50 - $84.50/hr

Website: About the Role We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be ...

JazzX AI

Senior Staff AI Engineer

San Francisco, CA

$65 - $84/hr

Website: About the Role We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be ...

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Postdoctoral In Reinforcement Learning information

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.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

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 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.
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What job categories do people searching Postdoctoral In Reinforcement Learning jobs in California look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in California are:
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Postdoctoral In Reinforcement Learning jobs near you
Senior Machine Learning Engineer, Reinforcement Learning - Egofold

Senior Machine Learning Engineer, Reinforcement Learning - Egofold

Snail Games USA

Beverly Hills, CA • On-site, Remote

$150K - $185K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 18 days ago

Job description

Senior Machine Learning Engineer, Reinforcement Learning – Egofold

About Snail Games USASnail Games strives to create the new high bar for gameplay experience in online gaming. We have been a global developer and publisher of digital entertainment since 2009 and are committed to pushing the boundaries of the industry.

About EgofoldEgofold is an AI initiative within Snail Games focused on intelligent agents, simulation, and AI-driven workflows for interactive products. It operates with startup-style speed and broad ownership, backed by an established game company, and is currently building practical prototypes while shaping its longer-term direction.

About the RoleWe are looking for a Senior Machine Learning Engineer with strong depth in machine learning and practical experience applying reinforcement learning and related methods to agent behavior and decision systems. This role is focused on the ML core of Egofold: designing experiments, training and improving models, shaping evaluation loops, and helping successful approaches become usable parts of the broader project.

This is not a siloed research role. The best candidates stay engaged through evaluation, iteration, and practical integration, and bring enough adjacent breadth to be effective in a small, collaborative team. We value curiosity, ownership, sound judgment, and respectful, low-ego collaboration.

Job Type: Full-TimeLocation: Hybrid – Los Angeles Area (1–2 in-office meetings per month)

Responsibilities

  • Design, train, and iterate on machine learning models for intelligent agents and decision-making systems, with an emphasis on reinforcement learning and related approaches.

  • Define and refine state representations, action spaces, reward structures, and evaluation criteria to improve agent behavior.

  • Build and improve practical experimentation and training workflows, including data generation, experiment tracking, and reproducibility.

  • Analyze results, debug model behavior, and make pragmatic tradeoffs between model performance, iteration speed, and system complexity.

  • Work closely with engineers and other partners to help integrate successful ML work into usable product systems.

  • Contribute thoughtful technical input on next-step experiments, tooling, and ML direction as Egofold continues to evolve.

Minimum Requirements

  • Strong foundation in machine learning, with hands-on experience building, training, and iterating on applied ML systems.

  • Professional or substantial project experience with reinforcement learning, agent-based systems, sequential decision-making, or closely related areas.

  • Strong Python skills and experience with modern ML frameworks such as PyTorch.

  • Experience designing experiments, evaluating model behavior, and improving results through systematic iteration.

  • T-shaped capability: deep machine learning expertise plus practical range across one or more adjacent areas such as simulation, evaluation, model integration, systems collaboration, or robotics-adjacent machine learning.

  • Strong problem-solving ability, sound judgment, and comfort working in ambiguous, fast-changing environments.

  • Respectful, low-ego collaborative style and willingness to work beyond a narrow specialty when the work requires it.

Nice to Have Any of the following are valuable, but we do not expect depth in every area:

  • Experience with reinforcement learning methods such as PPO, SAC, DQN, actor-critic, or related approaches.

  • Familiarity with simulation environments, multi-agent systems, game AI, or interactive agent behaviors.

  • Familiarity with C++, inference runtimes, or collaborating with engineers who deploy machine learning models into production systems.

  • Exposure to robotics, embodied AI, or embedded / on-device machine learning constraints.

Salary Range: $150,000 – $185,000 Annually

Why Join the Snail Games USA Team?

  • True focus on work/life balance

  • Paid company holidays, vacation, and separate sick leave

  • Medical, dental, vision, and Life/LTD

  • 401k with company match

Work Authorization Requirements

Applicants must be legally authorized to work in the United States at the time of application. This position does not offer visa sponsorship now or in the future (including H-1B).

Additional Information

As part of the Company’s activities in video game development, publishing, and short-form video content creation, certain projects, discussions, or creative materials may include themes, visuals, language, or subject matter that some individuals could find mature, violent, sexual, graphic, or otherwise sensitive in nature (collectively referred to as “Mature Content”). Examples may include, but are not limited to, depictions or descriptions of combat, violence, adult themes or relationships, suggestive or satirical humor, or strong language. Employees are expected to engage with such material in a professional and creative context as part of their job duties.

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