Senior Embedded Machine Learning Engineer

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  .

Role Description

We are looking for a highly skilled Embedded Engineer who can translate advanced sensor algorithms and machine learning models into efficient, production-ready C/C++ implementations optimized for extremely resource-constrained environments. You will work closely with ML scientists and firmware teams to bring cutting-edge signal processing capabilities and ML models onto embedded platforms with strict memory, computing and power budgets.

Responsibilities

• Convert sensor algorithms and build ML inference pipelines into efficient embedded C/C++ code for microcontrollers or other constrained platforms.
• Optimize code for memory footprint, CPU usage, and real-time performance.
• Co-develop with algorithm / ML researchers to refine models for embedded deployment.
• Profile runtime behavior, identify bottlenecks, and perform low-level debugging.
• Work with firmware teams to integrate sensor algorithms / ML models into system software.
• Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.

Required Skills

• BS/MS in Electrical Engineering, Computer Engineering, Computer Science, or related field.
• Strong proficiency in C/C++ for embedded systems.
• Ability to read/translate algorithmic descriptions in Python into low-level codes.
• Experience translating and optimizing machine learning models for embedded targets (e.g., quantization, fixed-point, pruning).
• Understanding basic DSP concepts (filters, FFTs, spectral processing, etc.)
• 2+ years of experience pushing sensor algorithm or ML models to production (C++)
• Solid software engineering skills and proficiency in Python

Bonus Skills

• Experience in common ML libraries (TensorFlow, PyTorch, Boosted Training, etc.)
• Experience working in resource-restricted systems.
• Experience with ARM Cortex-M or similar MCUs and on-device ML frameworks (CMSIS-NN, etc.).
• Knowledge of low-level optimization techniques such as pipeline-aware coding, and memory layout optimization, etc.

$180,000 - $200,000 a year

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!

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