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Summer Reinforcement Learning Intern Jobs in Riverside, CA

Lifelong Learning

Special Education Teacher

Hesperia, CA · On-site

$79K - $100K/yr

Knowledge and understanding of Independent Study, Personal Learning, and Distance Learning ... reinforcement. * Ability to make independent judgments, meet deadlines and maintain accurate ...

Lifelong Learning

Special Education Teacher

Rialto, CA · On-site

$79K - $100K/yr

Knowledge and understanding of Independent Study, Personal Learning, and Distance Learning ... reinforcement. * Ability to make independent judgments, meet deadlines and maintain accurate ...

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All JobsSummer Reinforcement Learning Intern JobsSummer Reinforcement Learning Intern Jobs in Riverside, CA

Summer Reinforcement Learning Intern information

See Riverside, CA salary details

$9

$17

$25

How much do summer reinforcement learning intern jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for summer reinforcement learning intern in Riverside, CA is $17.77, according to ZipRecruiter salary data. Most workers in this role earn between $15.05 and $20.05 per hour, depending on experience, location, and employer.

What types of projects or tasks can I expect as a Summer Reinforcement Learning Intern?

As a Summer Reinforcement Learning Intern, you can expect to work on projects ranging from implementing and testing RL algorithms to analyzing experiment results and optimizing model performance. Interns often collaborate with experienced researchers and engineers, contributing to both independent and team projects. You may also be involved in literature reviews, setting up simulation environments, and presenting findings to your team. The role provides hands-on experience with real-world RL applications, and you’ll have the opportunity to learn from feedback and mentorship throughout your internship.

What is the difference between Summer Reinforcement Learning Intern vs Summer Data Science Intern?

AspectSummer Reinforcement Learning InternSummer Data Science Intern
Required CredentialsUndergraduate or graduate in CS, AI, or related fields; some knowledge of machine learning and programmingUndergraduate or graduate in Data Science, Statistics, or related fields; strong analytical and programming skills
Work EnvironmentResearch-focused, experimental projects, often in AI and machine learning teamsData analysis, modeling, visualization, and reporting tasks across various departments
Employer & Industry UsageTech companies, AI startups, research labsTech firms, finance, healthcare, and consulting industries

The Summer Reinforcement Learning Intern role focuses on developing and testing reinforcement learning algorithms, often within AI research teams. In contrast, the Summer Data Science Intern role involves broader data analysis and modeling tasks. Both roles require programming skills and are common in tech industries, but they differ in their specific focus and project types.

What are Summer Reinforcement Learning Interns?

Summer Reinforcement Learning Interns are students or recent graduates who work temporarily, usually during the summer, to gain hands-on experience in reinforcement learning, a subfield of machine learning. Their responsibilities often include assisting with the development and testing of algorithms, analyzing data, and collaborating with research teams on projects related to artificial intelligence. This role provides an opportunity to apply theoretical knowledge from coursework to real-world problems, often resulting in valuable skills and networking opportunities for future careers in AI or data science.

What are the key skills and qualifications needed to thrive as a Summer Reinforcement Learning Intern, and why are they important?

To thrive as a Summer Reinforcement Learning Intern, you need a solid background in computer science, mathematics (particularly probability and linear algebra), and experience with machine learning frameworks. Familiarity with Python, TensorFlow or PyTorch, and a strong grasp of reinforcement learning algorithms are typically required, often supported by coursework or relevant certifications. Strong problem-solving skills, curiosity, and effective communication help you stand out in collaborative research and fast-paced project environments. These skills are crucial for contributing to innovative AI projects, rapidly learning new concepts, and effectively sharing findings with mentors and team members.
What are popular job titles related to Summer Reinforcement Learning Intern jobs in Riverside, CA? For Summer Reinforcement Learning Intern jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Summer Reinforcement Learning Intern jobs in Riverside, CA look for? The top searched job categories for Summer Reinforcement Learning Intern jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Summer Reinforcement Learning Intern jobs? Cities near Riverside, CA with the most Summer Reinforcement Learning Intern job openings:
Summer Reinforcement Learning Intern jobs near you
Infographic showing various Summer Reinforcement Learning Intern job openings in Riverside, CA as of May 2026, with employment types broken down into 100% Internship. Highlights an 100% In-person job distribution, with an average salary of $36,969 per year, or $17.8 per hour.

Robotics Research Internship-Locomotion & Planning (Summer 2026)

FieldAI

Irvine, CA • On-site

$45 - $60/hr

Internship

Posted 22 days ago

Job description

About the Internship
Field AI is building the future of autonomy-from rugged terrain to real-world deployment. We're on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments.
We are offering a Summer 2026 internship focused on learning-based locomotion and planning for PhD students interested in advancing autonomous legged robot capabilities. As a research intern, you will work at the intersection of reinforcement learning, locomotion control, and learned planning, developing integrated systems that enable robots to move and navigate intelligently through complex, unstructured environments.
You will collaborate closely with Field AI research scientists and engineers to design experiments, develop locomotion and planning systems, and validate ideas in simulation and on real hardware. This internship emphasizes building tightly integrated learning-based systems that connect low-level locomotion with high-level planning, translating research into practical, deployable capabilities for real-world robotics.
What You'll Get To Do
Advance RL-Based Locomotion and Learned Planning Research
  • Design, implement, and evaluate reinforcement learning pipelines that tightly integrate locomotion control with learning-based planning.
  • Explore how learned planners can inform and adapt locomotion behaviors across varied terrain and dynamic conditions.
  • Contribute to research projects from early-stage ideas through simulation experiments and on-robot validation.

Bridge Locomotion and Planning Across the Sim-to-Real Gap
  • Develop and refine sim-to-real transfer strategies, including domain randomization, system identification, and adaptive methods, for integrated locomotion-planning systems.
  • Build and leverage GPU-accelerated simulation environments (Isaac Gym, Isaac Lab, MuJoCo) for scalable training and evaluation.
  • Test and iterate on policies using real legged robot platforms in unstructured environments.

Build Systems That Connect Research to Deployment
  • Translate research concepts into working robotic systems tested on real hardware.
  • Develop experimental setups and tooling to support data collection, evaluation, and reproducibility.
  • Help ensure locomotion and planning systems are robust, field-relevant, and ready for iterative improvement.

Collaborate Across the Full Robotics Stack
  • Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
  • Incorporate real-world telemetry and field data to refine models and improve generalization.
  • Engage with researchers and engineers across the team to align experiments with broader autonomy goals.

Rapidly Iterate and Learn
  • Prototype quickly, run experiments in simulation and on hardware, and analyze results rigorously.
  • Balance exploratory research with concrete deliverables over the course of the internship.
  • Debug system-level issues spanning simulation, software, hardware, and learning.

What You Have
  • Current PhD student in Robotics, Computer Science, Mechanical Engineering, AI/ML, or a closely related field.
  • Research experience in reinforcement learning for continuous control, locomotion, or learning-based planning.
  • Strong foundation in contact dynamics, control theory, and kinematics.
  • Proficiency in Python and/or C++, with experience using robotics or ML tooling.
  • Familiarity with physics-based simulators such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet.
  • Experience designing experiments and evaluating results on robotic systems (simulation or hardware).
  • Curiosity, initiative, and a strong interest in building autonomous systems that operate in the real world.

The Extras That Set You Apart
  • Hands-on experience with legged robot platforms (quadrupeds, wheeled-quadrupeds, bipedal systems, or exoskeletons).
  • Experience with sim-to-real transfer for locomotion or planning policies.
  • Background in learning-based planning, motion planning, or terrain-adaptive control.
  • Familiarity with ROS or ROS2.
  • Publications, preprints, or open-source contributions in locomotion, RL, planning, or control.
  • Experience deploying neural network controllers on resource-constrained or real-time robotic platforms.
  • Interest in bridging cutting-edge research with practical, field-ready robotic systems.

$45 - $60 an hour
Our salary range is generous and we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience.
Field AI Onsite Work Philosophy
At Field AI, we believe the most effective way to collaborate and solve complex challenges is by working together in person. This is a fully onsite role, and candidates will be expected to work from our Irvine, CA office. In-person engagement is essential to our success, and we offer flexible working hours to support focus and work-life balance.
Why Join Field AI?
We are solving one of the world's most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.
Be Part of the Next Robotics Revolution
To tackle such ambitious challenges, we need a team as unique as our vision - innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We're seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.
Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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