Machine Learning Researcher

• 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, particularly those relevant to perception, localization, and scene understanding.
• 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• Build on our unpublished research and state of the art approaches for geometric computer vision.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, ensuring new developments are thoughtfully incorporated into our technology roadmap.
• Conduct exploratory experiments on novel approaches; document and share findings that inform future production work.
• Participate in project planning, reviews, and retrospectives, contributing technical expertise and mentorship where appropriate.
• Publish original research.

• M.S. or Ph.D. in Machine Learning, Computer Science, or a related field or equivalent experience demonstrating deep fluency in modern ML methods.
• 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, high-impact solutions.
• Clear written and verbal communication skills for articulating technical concepts to both research and engineering audiences.

Nice to have

• Publications in major ML or AI conferences or equivalent contributions to applied ML research.
• Active engagement with the ML research community, staying current with conferences, open-source releases, and emerging methods., This position requires access to technology controlled under the International Traffic in Arms Regulations (ITAR). Applicants must be a U.S. person as defined under ITAR (U.S. citizen, lawful permanent resident, asylee, or refugee) or qualify for a license exception.

Compound Eye Applicant and Worker Privacy Notice

• Base salary: $175,000 to $300,000, depending on experience.
• Strong Incentive Stock Options (ISO)
• Medical, dental, and vision insurance
• Annual home office stipend
• Employer-paid long-term disability, 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

We’re looking for a great engineer to join our US team of 20. Our mission is to teach machines to see. Our culture is based on transparency, mutual respect, mutual accountability, and valuing great ideas no matter where they come from. We already have revenue and our Series A round was led by a tier one VC. We are a remote-first company with employees across the United States. We had remote employees long before the pandemic and our team is now distributed across three states. We will ensure you have everything you need to work from home and the option to work from an office when it is safe to do so. We also offer competitive benefits including comprehensive health care plans, 401k with matching, flexible schedules, and discretionary PTO.

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.