About the job
About Sesame
At Sesame, we envision a future where technology feels alive—capable of seeing, hearing, and interacting with us in a manner that is both intuitive and human. We are pioneering a new generation of computers that seamlessly integrate voice agents into everyday life. Our talented team comprises industry trailblazers from Oculus, Ubiquity6, Meta, Google, and Apple, bringing a wealth of expertise in both hardware and software development. Join us to redefine the interaction between humans and machines.
About the Role
We are on the lookout for an innovative Embedded Machine Learning Engineer specializing in gesture recognition to facilitate rich, dependable interactions on wearable devices. The ideal candidate will thrive in a dynamic environment and be adept at transforming concepts from ideation to tangible products that users can experience. Collaborating closely with our hardware, firmware, and product teams, you will ensure that user interactions remain fluid and consistent across various settings.
Key Responsibilities:
Design, implement, and deploy algorithms for gesture detection optimized for ultra-low-power embedded hardware.
Adapt and refine larger machine learning models for deployment on mobile-class devices.
Oversee the entire development lifecycle: system design, data gathering and curation, synthetic data generation, model training and assessment, and on-device optimization.
Collaborate with electrical, mechanical, and product teams to integrate algorithms into evolving hardware frameworks.
Research and select promising methodologies from existing literature, and innovate new approaches when necessary to achieve our distinctive objectives.
Essential Qualifications:
A minimum of 10 years of experience in Software Engineering, Machine Learning Research, or a related field.
Demonstrated ability to operate with high autonomy in ambiguous environments.
Proven track record of developing and deploying machine learning algorithms on embedded or resource-constrained devices.
Strong proficiency in Python and C/C++, with experience in frameworks such as PyTorch or TensorFlow.
Hands-on experience with end-to-end machine learning workflows, from data acquisition to on-device deployment.
A solid understanding of signal processing and/or time-series analysis methods for sensor data.
Exceptional communication skills, with the ability to articulate complex ideas clearly.

