ZipRecruiter

Log In

1

Reinforcement Learning Internship Jobs in Austin, TX

Apple

Hardware Systems Engineering

Austin, TX

$122K - $161K/yr

... and reinforcement learning, as well as other related areas such as accessibility, privacy, and ... Prior internship(s), group or personal project exposure, TA and/or work experience. This posting is ...

Apple

Hardware Systems Engineering

Austin, TX

$122K - $161K/yr

... and reinforcement learning, as well as other related areas such as accessibility, privacy, and ... Prior internship(s), group or personal project exposure, TA and/or work experience. This posting is ...

Apple

Hardware Systems Engineering

Austin, TX

$122K - $161K/yr

... and reinforcement learning, as well as other related areas such as accessibility, privacy, and ... Prior internship(s), group or personal project exposure, TA and/or work experience. This posting is ...

Apple

Hardware Systems Engineering

Austin, TX · On-site

$122K - $161K/yr

... and reinforcement learning, as well as other related areas such as accessibility, privacy, and ... Prior internship(s), group or personal project exposure, TA and/or work experience. This posting is ...

All JobsReinforcement Learning JobsReinforcement Learning Internship JobsReinforcement Learning Internship Jobs in Austin, TX

Reinforcement Learning Internship information

See Austin, TX salary details

$8

$16

$24

How much do reinforcement learning internship jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for reinforcement learning internship in Austin, TX is $16.89, according to ZipRecruiter salary data. Most workers in this role earn between $14.28 and $19.04 per hour, depending on experience, location, and employer.

What is a reinforcement learning internship?

A Reinforcement Learning Internship is a temporary position, often for students or recent graduates, where you work on projects involving reinforcement learning—a type of machine learning where agents learn by interacting with their environment to achieve goals. Interns typically assist with research, data analysis, algorithm development, and experimentation under the supervision of experienced professionals. This role provides hands-on experience with RL frameworks, coding in languages like Python, and exposure to real-world applications such as robotics, gaming, or autonomous systems. The internship helps build practical skills and can pave the way for advanced study or a career in artificial intelligence research.

What are some common challenges faced during a reinforcement learning internship and how can I prepare for them?

As a Reinforcement Learning Intern, you may encounter challenges such as tuning hyperparameters, managing computational resources, and understanding the intricacies of reward design. Interns often work with large datasets and complex environments, which can be resource-intensive and require efficient coding skills. To prepare, it's helpful to familiarize yourself with popular RL frameworks (like TensorFlow or PyTorch), brush up on mathematical concepts such as Markov Decision Processes, and practice implementing algorithms from academic papers. Collaboration with senior researchers and regular code reviews are also key aspects of the internship experience.

What are the key skills and qualifications needed to thrive as a reinforcement learning intern, and why are they important?

To thrive as a Reinforcement Learning Intern, you need a strong background in mathematics (especially probability, statistics, and linear algebra), programming proficiency (commonly in Python), and foundational knowledge of machine learning concepts. Experience with libraries and frameworks such as TensorFlow, PyTorch, OpenAI Gym, and familiarity with relevant research papers or coursework are highly beneficial. Analytical thinking, creativity, and effective communication skills help interns solve complex problems and collaborate with research teams. These skills are crucial for contributing to innovative RL projects and efficiently learning from real-world experimentation.

What is the difference between Reinforcement Learning Internship vs Machine Learning Internship?

AspectReinforcement Learning InternshipMachine Learning Internship
Required SkillsReinforcement learning algorithms, Python, data analysisSupervised/unsupervised learning, Python, data preprocessing
Work EnvironmentResearch labs, AI startups, tech companiesTech firms, research institutions, data-driven companies
Industry UsageSpecialized in decision-making models and sequential learningBroader applications including classification, regression, clustering

Reinforcement Learning Internship focuses on decision-making algorithms and sequential learning, often in research or AI startup environments. Machine Learning Internship covers a wider range of algorithms and applications, suitable for various industries. Both roles require programming skills and a background in data science, but reinforcement learning internships are more specialized in AI decision systems.

What are the most commonly searched types of Reinforcement Learning jobs in Austin, TX?

The most popular types of Reinforcement Learning jobs in Austin, TX are:

What are popular job titles related to Reinforcement Learning Internship jobs in Austin, TX?

For Reinforcement Learning Internship jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Internship jobs in Austin, TX look for?

The top searched job categories for Reinforcement Learning Internship jobs in Austin, TX are:

What cities near Austin, TX are hiring for Reinforcement Learning Internship jobs?

Cities near Austin, TX with the most Reinforcement Learning Internship job openings:

Reinforcement Learning Internship jobs near you

Senior Reinforcement Learning Engineer

Gravis Robotics

Austin, TX

$103K - $142K/yr

Full-time

Posted 13 days ago

Job description

Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots. 

Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment. 

Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.

About the Job
 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. You will build control modules that run on many different machines, across many sites, with different soil conditions. We’re looking for a roboticist with data driven planning and/or control background, deep python expertise and good level of C++ proficiency.
 
To be successful in this role you should have experience working with real robots, tackling the challenges of sim2real transfer, and deploying robotic systems in a production environment.
 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. In this role, you will develop control modules designed to run across diverse machines, sites, and soil conditions. We are looking for a roboticist with a background in data-driven planning and/or control, strong Python skills, and a solid working knowledge of C++.
 
To thrive in this role, you should have experience working with physical robots, navigating the challenges of sim-to-real (sim2real) transfer, and deploying robotic systems into production environments.
What you will do
Learning-Based Planning and Control for Real Systems
 
  • Develop data driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions
  • Contribute to  simulation improvements that reduce or address the sim2real gap
  • Define data collection and curation pipelines for incorporating real data in policy training 
  • Design experiments focused on continuous performance and robustness improvements.
  • Explore the usage of adaptive and online reinforcement learning in deployed systems
  • Provide mentorship and supervision for junior team members, interns, and students.
 
System Integration
 
  • Integrate learned components into a larger software stack
  • Collaborate with excavation and motion planning engineers
  • Build tools for analysing and evaluating the behavior of learned components
What we’re looking for

We recognize that excellent candidates come from diverse backgrounds with various combinations of skills. If you meet most of the core qualifications below, we highly encourage you to apply.

Core qualifications

  • 2–5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots with a customer. If you only have experience with simulation, you’re most likely not a good fit for this position.

  • Experience with GPU accelerated simulation environments (e.g. IsaacSim/IsaacLab, CARLA, MuJoCo)

  • Strong Python skills and experience with PyTorch or similar libraries

  • Proficiency in C++

  • Comfortable debugging real-world system behavior

  • Ability and willingness to travel as required by business projects. 

Great-to-Have Skills & Experience

  • Experience with hydraulic machinery

  • Experience with supervised learning or imitation learning

  • Research experience in reinforcement learning

  • Experience deploying robotic systems at scale (e.g. hundreds of units)

  • Familiarity with ROS or similar robotics frameworks

  • Experience with feature-flagged deployments, staged rollouts, or long-lived platforms

  • Experience with data curation for ML applications

  • Experience guiding, mentoring, or leading junior colleagues, students, or project teams. 

  • Familiarity with or interest in utilizing AI coding tools. 

This Role is a Great Fit If

  • You are passionate about building systems that work reliably in the real world

  • You want to help build a long-lived excavation planning and control system intended to scale and positively impact the entire construction industry. 

  • You are comfortable working with the realities of imperfect data and noisy measurements.

  • You have a keen interest in bridging the sim2real gap and understanding the differences between simulation and physical environments. 

  • You are excited to help drive technical direction in a growing team transitioning from prototyping to the product stage.

  • You value a collaborative team culture rooted in thoughtful design, creative thinking, mutual respect, and pragmatism. 

Don't meet every requirement? If you're enthusiastic about this role but your experience doesn't match every qualification, we still encourage you to apply. You might be the perfect candidate for this or other positions. This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction.
 
Gravis Robotics offers a fair market salary and a working location in the vibrant city of Zurich. As a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals:
 
If you are a highly qualified candidate with the requisite skills and experience, we encourage you to apply and discuss your preferred working arrangement during the interview process. Gravis is an equal opportunity employer.
 
We are committed to building an inclusive and diverse team, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. We are an international team that is working to solve problems with a global impact: to facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles.
 
 
 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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.