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Postdoctoral In Reinforcement Learning Jobs in Texas

Senior Reinforcement Learning Engineer

Austin, TX ยท On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will leverage their expertise in reinforcement learning to solve locomotion and manipulation challenges, mentor junior engineers, and implement advanced ...

Senior Reinforcement Learning Engineer

Austin, TX ยท 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 ...

This role combines deep expertise in both computer vision and large language models with hands-on experience in reinforcement learning to create intelligent systems that can understand, reason about ...

Machine Learning Lead

Austin, TX ยท On-site +1

$54.75 - $75/hr

... Reinforcement Learning for heterogeneous agent coordination--that enable our platform to optimize deliveries across AVs, humanoid robots, and delivery bots in real-time. You'll work directly with our ...

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Postdoctoral In Reinforcement Learning information

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.

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 popular job titles related to Postdoctoral In Reinforcement Learning jobs in Texas? For Postdoctoral In Reinforcement Learning jobs in Texas, the most frequently searched job titles are:
What cities in Texas are hiring for Postdoctoral In Reinforcement Learning jobs? Cities in Texas with the most Postdoctoral In Reinforcement Learning job openings:
Senior Reinforcement Learning Engineer

Senior Reinforcement Learning Engineer

Apptronik

Austin, TX โ€ข On-site

$103K - $142K/yr

Full-time

Posted 8 days ago


Job description

Job Summary:
Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. The Senior Reinforcement Learning Engineer will leverage their expertise in reinforcement learning to solve locomotion and manipulation challenges, mentor junior engineers, and implement advanced learning algorithms for the company's humanoid robots.
Responsibilities:
โ€ข Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
โ€ข Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
โ€ข Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
โ€ข Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
โ€ข Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
โ€ข Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
โ€ข Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
Qualifications:
Required:
โ€ข Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
โ€ข Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
โ€ข Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
โ€ข A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
โ€ข A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
โ€ข A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.
โ€ข A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
โ€ข A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
โ€ข Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
โ€ข A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.
Company:
Apptronik is a robotics company that designs and builds humanoid robots for various real-world applications. Founded in 2016, the company is headquartered in Austin, USA, with a team of 51-200 employees. The company is currently Growth Stage.