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Postdoctoral In Reinforcement Learning Jobs (NOW HIRING)

People In AI

Staff MLE, Reinforcement Learning

Fremont, CA · On-site

$270K - $280K/yr

This is a Staff-level role for a Machine Learning Engineer who combines real depth in reinforcement learning and post-training with strong production software engineering. The company is looking for ...

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

As of Sep 13, 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 September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Staff MLE, Reinforcement Learning

Fremont, CA • On-site

People In AI
See more jobs at People In AIexternal

$270K - $280K/yr

Other

Posted 5 days ago

Job description

Staff MLE, Reinforcement Learning

Compensation: $270,000 - $280,000 base + equity

Location: San Francisco, hybrid 3 days per week


Join a fast-growing AI technology company building the infrastructure, training environments, and evaluation systems used to improve advanced AI models.


This is a Staff-level role for a Machine Learning Engineer who combines real depth in reinforcement learning and post-training with strong production software engineering. The company is looking for someone who can operate across experimentation, model improvement, infrastructure, and technical leadership while remaining deeply hands-on.


The Mission

The company is building systems that help advanced AI models learn, improve, and perform reliably on increasingly complex tasks.


That means creating reinforcement learning environments, generating high-quality training signal, evaluating model behavior, building reliable graders and verifiers, and developing the infrastructure required to run large-scale training and evaluation workflows.


The work sits much closer to the underlying models and training lifecycle than traditional AI application development.


The Role

You will sit between research engineering and production Machine Learning Engineering, combining hands-on experimentation with Staff-level technical ownership.

You will work on problems across reinforcement learning, post-training, agent training, evaluation, and ML infrastructure. Projects move quickly, and you may move between running experiments, designing systems, writing production code, setting technical direction, and leading other engineers through ambiguous technical problems.


The existing team has strong implementers. This hire is intended to bring another level of technical judgment, helping determine what should be built, how it should be designed, and how the team should execute.


What You'll Do

  • Design and build reinforcement learning environments for agentic tasks.
  • Develop task definitions, tool interfaces, reward structures, state management, and evaluation logic.
  • Build post-training and fine-tuning pipelines across supervised fine-tuning and reinforcement learning.
  • Develop verifiers, graders, rubrics, and evaluation systems for complex model behavior.
  • Run and diagnose model-training experiments, including issues around reward quality, data quality, and training signal.
  • Build infrastructure capable of running large numbers of model and agent trajectories.
  • Develop production-grade ML systems across orchestration, reliability, fault tolerance, and experiment management.
  • Translate ambiguous technical problems into clear architectures and execution plans.
  • Set technical direction and influence other engineers while remaining deeply hands-on.
  • Use modern AI development tools while maintaining strong engineering judgment around the resulting systems.


What You'll Bring

  • Strong hands-on Machine Learning Engineering experience.
  • Practical experience with model post-training or fine-tuning.
  • Experience with SFT and at least one RL or preference-optimization approach such as GRPO, PPO, DPO, or similar.
  • Experience with agent environments, model evaluation, reward design, verifiers, graders, or adjacent areas.
  • Strong Python skills and production software engineering fundamentals.
  • Experience with ML infrastructure, distributed systems, platform engineering, or data systems at scale.
  • Strong system design and architecture judgment.
  • Ability to diagnose why a model or training run is or is not improving.
  • Evidence of Staff-level technical leadership and influence across other engineers.
  • High agency and a track record of independently identifying important technical problems and driving them through to completion.
  • Comfort working in a fast-moving, ambiguous engineering environment.
  • Strong technical communication skills.


Why Join?

  • Work directly on reinforcement learning, post-training, agent evaluation, and advanced ML infrastructure.
  • Operate closer to the underlying model-development lifecycle than traditional AI application engineering.
  • Combine research-oriented ML problems with real production engineering responsibility.
  • Stay deeply hands-on while having meaningful Staff-level influence over architecture and technical direction.
  • Work across a broad range of rapidly evolving AI problems rather than being siloed into one narrow technical area.
  • Join an engineering culture that values technical judgment, ownership, speed, and individual impact.
  • Build systems focused on measurable model improvement rather than isolated demos or API integrations.


About People In AI

We partner with AI-first startups, scale-ups, and enterprise organizations to connect exceptional engineers with opportunities to build production AI systems, intelligent platforms, and the next generation of AI infrastructure.

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