Senior Applied Scientist, On-Device ML
Gridware
Apply to this job San Francisco, CA hybrid Until 8/22/2026 3+ years exp First posted March 18, 2026 Last posted March 18, 2026
Job description
About Gridware
Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.
Role Description:
We are seeking a Senior Applied Scientist, On-Device ML to design models that operate on multimodal time-series sensor data in highly resource-constrained environments. You will develop algorithms that balance accuracy with strict power and memory limits, helping advance the next generation of Gridware’s edge intelligence. This role blends applied research, model optimization, and low-level implementation in collaboration with hardware and firmware teams.
Responsibilities
- 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.
Required Skills
- 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.
Bonus Skills
- 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.
**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 candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!
Gridware Technologies Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state, or local law.
Benefits
Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
Paid parental leave
Alternating day off (every other Monday)
“Off the Grid”, a two week per year paid break for all employees.
Commuter allowance
Company-paid training
Compensation (from employer):
175000–205000 USD per year
About this role
Summary
Design and optimize machine learning models for resource-constrained embedded systems.
Job title
Senior Applied Scientist, On-Device ML
Experience level
3+ years
Minimum experience
3+ years exp
Industry
software
Location requirements
San Francisco; remote work not specified
Salary
$175k–$205k
Management role
No
Skills & keywords
Required skills
MS or PhD in computer science or electrical engineeringdeveloping and deploying ML modelsworking with sensors and time-series dataML architectureson-device algorithm design
Preferred skills
DSP algorithmsC/C++model porting from Pythonedge ML toolsquantizationmodel compression
Specializations
machine learningedge MLtime-series datamodel optimization
Locations
Structured locations inferred from the posting.
San Francisco, CA, USA
Hybrid City
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