Senior Applied ML Engineer, On-Device
San Francisco, CA - USA
Department:
Job Summary
- Execute end-to-end ML workflows including exploratory data analysis feature engineering model training evaluation and optimization.
- Design and evaluate machine learning and DSP algorithms that meet strict power memory and latency constraints on embedded hardware.
- Conduct research and literature reviews on edge ML resource-constrained inference and efficient training techniques.
- Partner closely with hardware firmware and product teams to ensure seamless integration of models into the full system.
- MS or PhD in Computer Science Electrical Engineering or a related technical field.
- 3 years of experience developing and deploying production ML models on-device.
- 3 years of applied research experience in ML or algorithm development.
- Hands-on experience working with physical sensors and modeling time-series data.
- Strong foundation in ML architectures and on-device algorithm design for real-world systems.
- Familiar with DSP algorithms and C/C for resource-constrained embedded systems.
- Experience porting ML models from Python frameworks to firmware-level implementations.
- Familiarity with edge ML tools quantization model compression or on-device inference strategies.
Required Experience:
Senior IC
About Company
**At this time, Gridware is unable to provide visa sponsorship or immigration support for this role. We’re only able to consider candidates who are currently authorized to work in the country of employment without visa sponsorship now or in the future.** This describes the ideal candi ... View more