CV and ML Engineer

About the role

You will work on cutting-edge computer vision and machine learning problems, developing algorithms and systems that enable 3D spatial understanding. This includes training and optimizing deep learning models, building data pipelines, running experiments, and integrating models into production software systems.

As a member of a fast-paced team, you have the unique and rewarding opportunity to shape upcoming products that will delight and inspire millions of people every day.

Responsibilities

• Develop and optimize deep learning models for 3D perception tasks
• Build and maintain training data pipelines
• Improve model quality and efficiency through experimentation
• Integrate trained models into real-time production systems

Minimum qualifications

• Education: Master's or equivalent practical experience in Computer Science, Computer Vision, Machine Learning, or related technical field
• Deep learning expertise: Demonstrated work in 3D vision or geometric deep learning
• Programming: Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX
• Research skills: Experience with rapid prototyping, reproduction, and validation of research ideas
• Foundation knowledge: Strong mathematical foundations in machine learning, computer vision, or related fields
• Foundation models: Experience with foundation model architectures and training methodologies
• Teamwork: Experience working effectively in a multi-functional, collaborative environment

Preferred qualifications

• Education: PhD, or equivalent practical experience, in Computer Science, Machine Learning, Computer Vision, or a related technical field
• Publication record: Either a publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, COLM, etc), or a strong track record of applying deep learning techniques to real-world products
• Distributed training: Experience with large-scale distributed training and model parallelism
• 3D computer vision: Familiarity with 3D computer vision concepts: multi-view geometry, depth sensing, camera models, or 3D reconstruction
• Data engineering: Experience with training data curation and pipeline engineering
• Model optimization: Experience with model optimization for production deployment
• Systems programming: Proficiency in C++ or experience integrating ML models into performance-critical systems
• Communication: Strong communication skills and ability to present research findings to both technical and non-technical audiences