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

Intrinsic is seeking a highly motivated and talented PhD intern to contribute to cutting-edge research in foundation models for pose estimation with potential for publication. This internship offers the opportunity to work on challenging problems at the intersection of computational imaging (depth, polarization, multi-view), computer vision, robotics, and large-scale model training. You will be part of a dynamic team pushing the boundaries of pose estimation technology in the field of industrial robotics.

How your work moves the mission forward

As a PhD Intern specializing in Foundation Models for Pose Estimation, you will:

Research and develop novel architectures, training methodologies and datasets for foundation models aimed at improving pose estimation accuracy, robustness, and generalization. Design and conduct experiments to assess the performance of developed models on relevant datasets. Collaborate with senior researchers and engineers to integrate research findings into practical applications. Publish research findings in top-tier conferences and journals (optional, but encouraged). Develop models that will be deployed in customer factories Located in Mountain View or Munich Skills you will need to be successful Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related field. Strong background in computer vision or machine learning Solid understanding of deep learning techniques and experience with deep learning frameworks (e.g., Jax, PyTorch). Proficiency in programming (e.g., Python). Skills that will differentiate your candidacy Prior experience with pose estimation datasets and evaluation metrics. Experience with training and evaluating large-scale models. Familiarity with foundation models (e.g., vision transformers, diffusion models) is highly desirable. Publications in top-tier vision / machine learning conferences

At Intrinsic, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

If you have a disability or special need that requires accommodation, please contact us at: candidate-support@intrinsic.ai.