As a research intern, you will work at the intersection of reinforcement learning, locomotion ... You will collaborate closely with Field AI research scientists and engineers to design experiments ...
As a research intern, you will work at the intersection of reinforcement learning, locomotion ... You will collaborate closely with Field AI research scientists and engineers to design experiments ...
Senior Software Engineer (Card Present)
Irvine, CA · On-site
$131.50K - $173.40K/yr
Experience integrating with payment terminal hardware and device management platforms, with an ... The Company prohibits unlawful discrimination against any job applicant, employee, or unpaid intern ...
Senior Software Engineer (Card Present)
Irvine, CA · On-site
$131.50K - $173.40K/yr
Experience integrating with payment terminal hardware and device management platforms, with an ... The Company prohibits unlawful discrimination against any job applicant, employee, or unpaid intern ...
Currently enrolled in a Bachelors degree in Computer Science, Computer Engineering, Mathematics ... For operations internships - understanding of desktop/laptop hardware and Microsoft software, some ...
Currently enrolled in a Bachelors degree in Computer Science, Computer Engineering, Mathematics ... For operations internships - understanding of desktop/laptop hardware and Microsoft software, some ...
Hardware Engineer Intern information
See Riverside, CA salary details
$11.54 - $13.29
2% of jobs
$13.29 - $15.05
4% of jobs
$16.80 is the 25th percentile. Wages below this are outliers.
$15.05 - $16.80
19% of jobs
$16.80 - $18.56
24% of jobs
The median wage is $18.67 / hr.
$18.56 - $20.31
17% of jobs
$21.37 is the 75th percentile. Wages above this are outliers.
$20.31 - $22.07
16% of jobs
$22.07 - $23.82
6% of jobs
$23.82 - $25.58
5% of jobs
$25.58 - $27.34
3% of jobs
$27.34 - $29.09
3% of jobs
$29.09 - $30.85
1% of jobs
$11
$20
$30
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$45 - $60/hr
Other
Posted 11 days ago
Job description
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.