ML Software Engineer
About the role
We are seeking a ML Software Engineer to bring state-of-the-art machine learning out of the lab and onto the device. Research models only matter to our prototypes if they run in real time, within tight compute, memory, and power budgets, on the embedded hardware that powers wearables. This role sits at that boundary: you will train and adapt models alongside research scientists, then own the work of optimizing, porting, and running them efficiently on embedded targets. The ideal candidate is equally comfortable in a PyTorch training loop and in a C/C++ inference runtime on a resource-constrained device. They understand that on-device ML is a co-design problem, where model architecture, quantization, and the target hardware all shape one another, and they can make the trade-offs required to get a research model running on real hardware quickly. Familiarity with audio ML, computer vision ML, or audio DSP is a strong plus, as many of our models operate on multimodal (audio and visual) sensor data. Our team cultivates an honest and considerate environment where self-motivated individuals thrive. We encourage a strong sense of ownership and embrace the ambiguity that comes with working on the frontiers of research.
Responsibilities
- Work side by side with research scientists to take ML models from research prototypes to efficient, real-time inference on embedded devices.
- Train, fine-tune, and adapt ML models with deployment constraints (latency, memory, power, compute) in mind