The Company

We are excited to partner with Gridware, a fast-growing grid-resiliency company in San Francisco. They exist to enhance and protect the mother of all networks: the electrical grid. The grid touches everything grinds to a halt, and the consequences can be dire: wildfires burn, land is destroyed, property is damaged, progress stops, and lives are lost.  

Their team of engineers has built an advanced sensing system to continuously analyze both the electrical and mechanical behavior of grid assets. Utilizing high precision sensor arrays, the system identifies and allows preemptive mitigation of faults. The technology has been proven with utilities to bolster safety, reliability, and reduce customer outage durations. The demand for power will only increase. They protect the grid of today while we build the grid of tomorrow.

Gridware is privately held and backed by the best climate-tech and Silicon Valley investors. They are headquartered in the Bay Area in northern California.

Your Impact (Responsibilities)

The Senior Applied Scientist specializing in ML DSP (Machine Learning with Digital Signal Processing) is responsible for evaluating and developing models for multimodal time series sensor data on heavily resource-constrained computation systems. The ideal candidate will possess deep knowledge of machine learning architectures, digital signal processing techniques, and algorithm design.

The Senior Applied Scientist will, as a starting point, be responsible for the following:

• Execute end-to-end ML projects from exploratory data analysis to feature engineering and model evaluation, and inform firmware implementation and deployment.
• Design and build physically-informed data augmentation and domain randomization algorithms.
• Conduct literature reviews and research on resource-constrained inference and training techniques.
• Work closely with cross-functional teams, including hardware engineers, firmware engineers, and product managers.

About You

• Computer Science or Electrical Engineering degree
• 4+ years of professional experience with production machine learning models
• 4+ years of professional experience with physical sensors and time series data modeling
• 4+ years of research experience
• Experience low-level languages and memory management
• Strong fundamentals in statistics and computer science