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Postdoctoral In Reinforcement Learning Jobs in Fullerton, CA

Amgen

Executive Director, Frontier AI

Los Angeles, CA

In this vital role as the Executive Director, Frontier AI you will be responsible for frontier AI ... Portfolio prioritization and investment recommendations across reinforcement learning, multi-modal ...

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Postdoctoral In Reinforcement Learning information

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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 Fullerton, CA is $61,577.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,100.00 and $69,400.00 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.

What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Fullerton, CA look for?

The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Fullerton, CA are:

What cities near Fullerton, CA are hiring for Postdoctoral In Reinforcement Learning jobs?

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Postdoctoral In Reinforcement Learning jobs near you

Principal Machine Learning Researcher (Physical AI)

Freeform

Los Angeles, CA • On-site

Full-time

Re-posted 11 days ago

Job description

Job Summary:
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts. They are seeking a Principal Machine Learning Researcher to lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system.
Responsibilities:
• Design and develop machine learning models for complex, multi-physics manufacturing processes.
• Develop hybrid modeling approaches that combine first-principles physics with data-driven learning.
• Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems.
• Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing.
• Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.
• Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes.
• Integrate learned models with physics-based simulation and digital twin frameworks.
• Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware.
• Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics.
• Guide the integration of machine learning models into production software and manufacturing workflows.
• Help define research direction and technical standards for machine learning applied to physical systems within the organization.
Qualifications:
Required:
• 5+ years of experience in machine learning, applied research, or related technical fields or a PhD in machine learning, applied mathematics, physics, robotics, controls, or a closely related discipline.
• Strong foundations in machine learning applied to physical systems, modeling, or control.
• Proficiency in Python and at least one systems-level programming language (C/C++ preferred).
• Experience working with large-scale, noisy, real-world datasets.
Preferred:
• MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline.
• Experience with hybrid physics–ML models, digital twins, or simulation-in-the-loop learning.
• Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems.
• Experience with image-based or sensor-based inference in industrial or scientific settings.
• Familiarity with computational geometry or geometric modeling.
• Comfort working across theory, experimentation, and deployment in tightly coupled systems.
• Ability to reason from first principles and translate theory into working models and systems.
Company:
Freeform is a 3D printing company offering metal 3D printing solutions for manufacturing companies. Founded in 2018, the company is headquartered in Los Angeles, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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