Applied Intuition
Research Engineer - Robot Learning
About this role
Applied Intuition seeks Research Engineers to advance robot learning and autonomous systems. You'll develop cutting-edge AI for robotics and self-driving applications, leveraging access to massive real-world datasets and deploying algorithms across production vehicles and robotic systems.
What you'll do
- Design and support robotic hardware setup and mechatronic systems for specialized applications
- Build large-scale robotic simulation environments for reinforcement learning training
- Prepare human data for robotic learning use cases
- Collaborate with Research Scientists on publications for top-tier conferences
- Deploy end-to-end learning algorithms for autonomous vehicles and robotic systems in production
- Work with engineering teams on ADAS, data, and simulation infrastructure
What they're looking for
- Python and PyTorch
- Computer vision
- Robotics systems and simulation
- Reinforcement learning
- Distributed machine learning training
- Foundation models or diffusion policies (preferred)
- Physics-aware reconstruction or deformable simulation (preferred)
- Video/Gaussian generation (preferred)
Benefits
- Competitive base salary with equity options and RSUs
- Comprehensive health, dental, vision, life and disability insurance
- 401k retirement plan with employer match
- Learning and wellness stipends
- Paid time off
- Flexible work arrangements with primarily in-office expectations
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Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Walk us through a project where you implemented reinforcement learning or diffusion policies for robotics. What were the key challenges and how did you address them?
- Describe your experience building or working with robotic simulation environments. How have you scaled them for training, and what frameworks did you use?