The last decade has been incredibly exciting for electric mobility. However, the electrification of the transportation industry has scaled at the expense of battery quality, leading to uncomfortably high rates of battery pack failure and multi-billion-dollar safety recalls. In our view, every headline of electric vehicles and e-bikes catching fire is a major impediment towards an all-electric future.
This is why Glimpse is on a mission to enable battery quality at scale.
After having spent a few years in the trenches of battery and electric vehicle factories at Tesla and Toyota, Glimpse’s cofounders developed a vision for a one-stop-shop battery Quality Management Solution, which they actualized through the launch of the Glimpse Portal in March 2024. Today, Glimpse counts some of the world’s largest battery producers and OEMs, such as Lucid Motors and NASA, among its customers.
As an early-stage engineer at Glimpse, you will shape the direction of the company and its product. Working closely with Glimpse's Chief Technology Officer, you will design and build computer vision algorithms that empower Glimpse's customers to quickly diagnose and improve the quality of their batteries. These automated algorithms will unlock key insights into our customers’ terabyte-scale imaging datasets and enable them to regain control over their manufacturing operations. These algorithms will serve as core IP of our product.
You’re fired up by:
Our culture and values:
A working draft of our culture and values aspirations can be found here .
Requirements
Relevant skills: Python image processing stack (numpy, scikit-image, OpenCV, pytorch, and equivalents)
You have:
Nice-to-have:
If you don’t meet 100% of the requirements, we still encourage you to apply!
Interview process:
Benefits
Location
We’re headquartered in Greentown Labs in Somerville, MA. As an early-stage startup working on hardware and software, we have a strong preference for being in the office more often than not. However, we’re happy to consider flexibility options for exceptional candidates.
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