Machine Learning Engineer: Multimodal Sensor Fusion
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Description
We seek a Machine Learning Engineer to join our team in designing and implementing advanced algorithms for multimodal sensor fusion. The ideal candidate brings proven expertise in translating cutting-edge research into production-ready solutions that delight users at scale.
In this role, you will collaborate closely with hardware, software and user-experience teams to develop world-class algorithms that advance the state of the art while creating deeply personal experiences for our users. You will drive the development of multimodal deep learning models optimized for edge deployment, leveraging sensor fusion techniques to enable intelligent, real-time perception in resource-constrained environments.
Your work will span the full spectrum from evaluating breakthrough research to solving complex real-world problems, architecting model efficiency strategies for on-device inference, and ensuring algorithms perform flawlessly in production at scale. You will be instrumental in pushing the boundaries of what is possible when sophisticated machine learning meets spatial computing, while establishing best practices and technical direction for your team.
Minimum Qualifications
B.S and 3 years of experience in software engineering, deep learning, sensor fusion, or related fields.
Experience in deep learning frameworks such as PyTorch
Proficiency in Python development, using best coding practices
Preferred Qualifications
Ph.D. or M.S., with 3+ years of industry experience in deep learning or related fields.
Excellent math skills, including linear algebra, computational science and optimization
Creativity and curiosity for solving highly complex problems
Excellent communication and collaboration skills
Hands-on experience of working with sensors and hardware
An interest in incubating ideas and taking them to finish
Experience shipping algorithms or publishing in top tier AIML conferences