About The RoleThe AI innovation team at Agility works on building and deploying next-generation robot foundation models and end-to-end policies on humanoid robots. Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to perform different real-world tasks. You will work on a team, running experiments on humanoid robots, and will research and implement methods which can be transferred into production.
About the Work- Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.
- Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.
- Design and implement new simulation environments and tasks to support training and deployment of control policies.
- Develop, design, and test imitation learning methods
- Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production
About You- 3+ years of experience developing and deploying learning-from-demonstration
- Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.
- Experience with modern learning-from-demonstration tools like DiffusionPolicy
- Experience with robot data collection, training, and testing on hardware to perform manipulation tasks.
- Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software
- MS in Robotics, Computer Science, or a related field.
Bonus Points- PhD in Robotics, Computer Science, or a related field.
- Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).
- Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques.
- Experience with modern reinforcement learning techniques for locomotion, manipulation, and whole-body control
- Experience with writing performant, high quality software in C++
This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.
The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.
Anticipated Salary Range
$185,000-$288,000 USD
In addition to base pay, our competitive total rewards package consists of the following for full-time employees:- 401(k) Plan: Includes a 6% company match.
- Equity: Company stock options.
- Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees.
- Benefit Start Date: Eligible for benefits on your first day of employment.
- Well-Being Support: Employee Assistance Program (EAP).
- Time Off:
- Exempt Employees: Flexible, unlimited PTO and 10 company holidays, including a winter shutdown.
- Non-Exempt Employees: 10 vacation days, paid sick leave, and 10 company holidays, including a winter shutdown, annually.
- On-Site Perks: Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations.
- Parental Leave: Generous paid parental leave programs.
- Work Environment: A culture that supports flexible work arrangements.
- Growth Opportunities: Professional development and tuition reimbursement programs.
- Relocation Assistance: Provided for eligible roles.
- Annual Discretionary Bonus: Provided for eligible roles.
All of our roles are U.S.-based. Applicants must have current authorization to work in the United States.
Agility Robotics does not accept unsolicited referrals from third-party recruiting agencies. We prioritize direct applicants and encourage all qualified candidates to apply directly through our careers page. If you are represented by a third party, your application may not be considered. To ensure full consideration, please apply directly.Apply Now: https://grnh.se/b444bbd04us
