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Reinforcement Learning Robotics Jobs in Texas (NOW HIRING)

... or reinforcement learning. - Publications in top-tier conferences or journals related to machine learning or robotics. - Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or Jax.

... or reinforcement learning. - Publications in top-tier conferences or journals related to machine learning or robotics. - Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or Jax.

... or reinforcement learning. - Publications in top-tier conferences or journals related to machine learning or robotics. - Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or Jax.

... Robotics, Computational Biology, Physics, ect. ALTERNATE EXPERIENCE General comment on degrees ... Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ...

Senior Compiler Engineer - AI

Austin, TX

$121K - $160K/yr

An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ... The ideal candidate brings broad experience across machine learning, including reinforcement ...

Senior Compiler Engineer - AI

Austin, TX · On-site

$121K - $160K/yr

An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ... The ideal candidate brings broad experience across machine learning, including reinforcement ...

Autonomy Engineer

Houston, TX · On-site

$97K - $128K/yr

Experience with semantic mapping and scene understanding for autonomous mobile robots * Experience with NVIDIA Isaac Lab for reinforcement learning environment setup and training * Experience with ...

... robotics, autonomous cars, uncrewed aerial vehicles, and smart manufacturing-is a massive, specialized growth vector that requires unparalleled compute power for simulation, reinforcement learning ...

Showing results 41-60

Reinforcement Learning Robotics information

What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?

One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.

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

To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.

What is reinforcement learning in robotics?

Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.

What is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData analysis, predictive modeling, AI applications

Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.

What cities in Texas are hiring for Reinforcement Learning Robotics jobs? Cities in Texas with the most Reinforcement Learning Robotics job openings:

Research Scientist, Simulation Agents

Waabi

Dallas, TX • On-site, Remote

$158K - $269K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 20 days ago


Job description

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.
With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai
The Behaviors team at Waabi develops cutting-edge simulation agents and scenario generation algorithms for Waabi World, our simulation platform. As a Research Scientist on the Behaviors team, you will work closely with our multidisciplinary team of research scientists and engineers to invent the next generation of models and algorithms that power Waabi World. Your work will define the scenarios that push our self-driving system to its limits, generate the training signal that makes them better, and form a core pillar of our scientific safety case to put them on the road.
You will...
- Own and pursue a research agenda to develop realistic and controllable simulation agents.
- Advance the state-of-the-art in imitation learning, reinforcement learning, generative models, foundation models, planning and search, and other related areas for simulation agents.
- Collaborate with our simulation, autonomy, and safety teams to define high-impact research problems, ship solutions into production, and drive progress towards Waabi's milestones.
- Mentor junior scientists and interns; foster a culture of scientific rigor and rapid experimentation.
- Publish high-impact research at top-tier conferences in machine learning or robotics.
Qualifications:
- Masters/PhD in machine learning, computer science, engineering, or a related field.
- Strong background in imitation learning and/or reinforcement learning.
- Publications in top-tier conferences or journals related to machine learning or robotics.
- Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or Jax.
Bonus:
- Experience in self-driving, traffic simulation, or a related field.
- Proven ability to take research from prototype to production systems.
- Strong software engineering skills, including experience with large-scale training or simulation.
The US yearly salary range for this role is: $158,000 - $269,000 USD in addition to competitive perks & benefits. Waabi US Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.
Perks/Benefits:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- As we grow, this list continues to evolve!
Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact!
Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
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.