Research Scientist, Sensors and Systems Research

Key Skills

Aartificial intelligence, Computer Vision (CV), Effective communication skills, Machine learning techniques, NeurIPS, Python Programming, PyTorch

Job Description

Research Scientist, Sensors and Systems Research Responsibilities

• Develop computer vision and machine learning solutions for intelligent sensing applications.
• Develop novel ML/CV solutions for distributed ML, efficient inference and learning, multi-modal learning, self-supervised learning and/or privacy perseverance.
• Plan, execute cutting-edge research and prototype with AR / VR systems.
• Foster collaboration with a diverse team including optical, silicon technology, sensor, system architecture, security, and ML domain experts.
• Actively engage, collaborate, and influence other research pillars and application teams at Reality Labs.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• PhD degree or equivalent experience in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field.
• Experience in an applied research environment (university or company), in the domain of low-power technologies, sensors, IC design and AI/ML algorithms.
• Proficiency in Python programming and hands-on experience with frameworks such as PyTorch.
• Exposure to deploying algorithms into energy-efficient sensing and computing systems.
• Demonstrated experience in developing machine learning models at scale, from inception to generating business impact.
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications

• Experience in transitioning machine learning-based applications from research to production, particularly in the realm of on-device machine learning.
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as Nature, NeurIPS, CVPR, ICML, ICCV, ACL, ISCA, ISSCC, or similar.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience working and communicating cross functionally in a team environment.
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).