Senior Machine Learning Researcher - Remote

Overview

Compound Eye enables machines to understand their surroundings in 3D and in real time using only passive sensors like cameras and IMUs. Our VIDAS™ technology takes video from automotive-grade cameras and outputs a dense, semantic 3D representation of the world that vehicles and robots can use to navigate their environment. VIDAS is a fully redundant alternative to LiDAR and radar. Compound Eye has customers in automotive, agriculture, healthcare, and defense and is backed by Khosla Ventures and other leading investors.

Compound Eye is looking for a researcher-engineer who thrives on experimentation. You read new papers for fun, know when to adopt and when to question the latest architectures, and can turn academic ideas into practical prototypes. You want to take on world-class problems in computer vision and robotics where you may have unlimited resources during training but challenging constraints at inference.

You’ll focus on hands-on experimentation, exploration, and deep technical contribution. You’ll run your own experiments, propose hypotheses, and share results that help guide our next generation of ML systems.

In Short

• Plan, design, and run experiments to evaluate and refine deep learning architectures and training methodologies.
• Research, implement, and adapt new algorithms and model architectures from recent ML papers.
• Collaborate closely with talented engineers to translate research insights into robust, deployable models.
• Build on our unpublished research and state of the art approaches for geometric computer vision.
• Stay current with the latest research in machine learning, deep learning, and AI.
• Conduct exploratory experiments on novel approaches; document and share findings.
• Participate in project planning, reviews, and retrospectives, contributing technical expertise.
• Publish original research.

Requirements

• M.S. or Ph.D. in Machine Learning, Computer Science, or a related field.
• Familiarity with version-controlled experimentation and MLOps tools.
• Demonstrated ability to read, interpret, and implement research papers.
• Experience independently running and evaluating experiments from dataset preparation through model training and analysis.
• Excellent problem-solving skills and the ability to translate research insights into practical solutions.
• Clear written and verbal communication skills for articulating technical concepts.

Benefits

• Strong Incentive Stock Options (ISO).
• Medical, dental, and vision insurance.
• Annual home office stipend.
• Employer-paid long-term and short-term disability, and life insurance.
• 401(k) with employer match after 6 months.
• Flexible PTO and 8 holidays + 2 week winter break.
• Paid family leave.
• Quarterly onsites in San Francisco.