Embedded ML Engineer – Gesture Recognition

Sesame is a company focused on creating lifelike computers that can see, hear, and collaborate with humans. They are seeking an Embedded ML Engineer to design and deploy algorithms for gesture detection on wearable devices, collaborating closely with hardware and product teams to enhance user interactions. Responsibilities: Design, train, and deploy algorithms for gesture detection on ultra-low-power embedded hardware; Evaluate and adapt larger ML models for running on mobile class hardware; Own the full development cycle: system design, data collection & curation, synthetic data generation, model training & evaluation, and on-device optimization; Collaborate with electrical, mechanical, and product teams to integrate algorithms with evolving hardware designs; Pick promising approaches from the literature to bet on, and create new approaches where necessary to achieve our unique goals Qualifications: 10 years of experience in Software Engineering, ML Research, or related fields; Experience working with a high degree of autonomy in ambiguous environments; Proven experience building and deploying ML algorithms on embedded or resource-constrained devices; Proficiency in Python and C/C++, with experience in frameworks such as PyTorch or TensorFlow; Hands-on experience with end-to-end ML workflows, from data capture to on-device deployment; Strong grasp of signal processing and/or time-series analysis for sensor data; Excellent communication skills and the ability to work collaboratively across disciplines; Bachelor's degree or higher in computer science, electrical engineering, machine learning, or related field Required Skills: Machine Learning, Embedded Systems, Python, C/C++, PyTorch, TensorFlow, Signal Processing, Time-Series Analysis, On-Device Deployment, Synthetic Data Generation, Autonomy