Staff/Senior Reinforcement Learning Engineer (Industrial Assembly) at Intrinsic

Munich, Bavaria, Singapore -

Full Time

Start Date

Immediate

Expiry Date

19 Mar, 26

Salary

0.0

Posted On

19 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Robotic Manipulation, Deep Learning, Python, C++, TensorFlow, PyTorch, Production-Grade Reliability, Cloud Training Workflows, Industrial Robotics, Multimodal Perception, Complex Manipulation, Data Augmentation, Customer Focus, Algorithm Development, Tech Transfer

Industry

Software Development

Description

Intrinsic is Alphabet’s bet aiming to reimagine the potential of industrial robotics. Our team believes that advances in AI, perception and simulation will redefine what’s possible for industrial robotics in the near future – with software and data at the core. Our mission is to make industrial robotics intelligent, accessible, and usable for millions more businesses, entrepreneurs, and developers. We are a dynamic team of engineers, roboticists, designers, and technologists who are passionate about unlocking the creative and economic potential of industrial robotics. Role As a Staff/Senior Reinforcement Learning Engineer at Intrinsic, you will lead the end-to-end lifecycle of developing and deploying RL for complex industrial assembly tasks. You will research and implement sample-efficient algorithms with a strong focus on production-grade reliability and robustness. Working within the Engineering organization, you will drive rapid tech transfer from research into production, taking full ownership of the performance and operational success of learned controllers in our customers’ factories worldwide. Leveraging Intrinsic’s world-class infrastructure and robotics capabilities, you will contribute to elevating AI manipulation from the experimentation stage to unlock novel industrial automation paradigms. How your work moves the mission forward Develop, mature, and deploy production-grade RL policies that solve contact-rich assembly tasks on real-world manufacturing lines using industrial robots. Transition cutting-edge manipulation research into robust, scalable production solutions with high velocity. Define the roadmap and take ownership of the future development of our Reinforcement Learning Algorithms stack for global industrial deployment. Contribute to our cloud and on-premises training workflows required for large-scale model/policy optimization. Skills you will need to be successful Domain and Industry Experience: PhD in applied Reinforcement Learning, Visuomotor Policy Learning, or a related field with a focus on robotic manipulation and industrial assembly, complemented by 3+ years of relevant industry experience. Deep Learning & Software: Advanced proficiency in JAX, Python, and C++. Extensive experience with TensorFlow or PyTorch. Scientific Excellence: A strong theoretical foundation in state-of-the-art algorithms, evidenced by high-impact publications in the robot learning community (e.g., CoRL, RSS, ICRA). Deployment Know-How: A proven track record of successfully transferring RL algorithms from the experimental stage to production-grade industrial applications. Skills that will differentiate your candidacy Advanced Policy Learning: Experience with Diffusion Policies, World Models, Vision-Language-Action (VLA) models, or Offline RL. Multimodal Perception and Control: Expertise in integrating tactile/force sensing (force control) and computer vision (visual servoing) for contact-rich tasks. Complex Manipulation: Experience handling soft or linear deformable objects and/or utilizing data augmentation for robust policy training. Large-Scale Training: Proficiency in training on massive multimodal datasets and managing cloud-based training workflows (e.g., Argo). Industrial Robotics Expertise: Hands-on experience with industrial-grade automation equipment and familiarity with safety standards in production environments. Customer Focus: Willingness to travel internationally to analyze customer use cases and deploy solutions under ambitious timelines. At Intrinsic, we are proud to be an equal opportunity workplace. Employment at Intrinsic is based solely on a person's merit and qualifications directly related to professional competence. Intrinsic does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. It is Intrinsic’s policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity. If you have a disability or special need that requires accommodation, please contact us at: candidate-support@intrinsic.ai.

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Responsibilities

Lead the development and deployment of reinforcement learning algorithms for complex industrial assembly tasks. Transition research into robust production solutions and take ownership of the performance of learned controllers in customer factories.