Our mission is to replace mining with reuse as the largest source of the next billion batteries. The problem is that batteries are expensive to reuse today because of non-standardized testing, expensive shipping, and dangerous disassembly by hand. We collect and sort old batteries, test how good they still are, and take them apart. We do this for all types of EV batteries using ML-driven analytics & intelligent robotics. Then we turn them into modular, directly accessible, and low-cost grid storage systems. If we can’t do this, we prepare them for recycling to turn them into raw materials to make new cells. Our vision is to build local factories around the world to become the largest manufacturer of used batteries and their raw materials to realize a truly clean energy revolution.
Reverse Platform
Reverse is the platform for storing, processing, and estimating data on battery health, lifetime, and value. Batteries need multiple tests to estimate their State of Health (like electrical, software, and non-intrusive) before we decide on servicing, repurposing, or recycling. The system stores large amounts of data from these tests from various sources of batteries. Analytics models will process the data in real-time to provide insights on lifetime and health. It allows automation of intelligent decision-making on 2nd battery life applications.
Job objective
You will architect and develop the software & data platform to test, process, and measure the State of Health of batteries to collect from non-intrusive test methods like X-ray and CT scans. It merges and compares data from the battery management system and lifecycle testing from various sources. You will design and build the algorithm, pipeline, and infrastructure that analyzes, decides and predicts data on battery health, lifetime, and residual value. This data will drive what to do with the battery next, like servicing, repurposing, or preparing for recycling.
How you will contribute
- Build and launch algorithms to intelligently decide on the next step in the battery's lifetime.
- Design data processing algorithms and infrastructure to collect, analyze and predict the lifetime of batteries.
- Merge, process, and compare data sets from testing, BMS, electrical testing methods, and X-ray or CT sources.
- Develop models on standardized State of Health, lifetime, and valuation models to decide on the best 2nd life application.
- Work with partners in battery testing and data software vendors to launch first products fast.
The skills & experience that you bring
- At least a B.Sc. in Applied Mathematics, Statistics, Computer Science, Machine Learning, or a similar field.
- 3-5 yrs experience in developing production-ready software with proven skills in C/C++, Python, and related software tools.
- 3-5 yrs experience in data science algorithms in Python, SQL, and Scala. Developed data & ML tools in Torch/TensorFlow.
- +3 yrs Experience deploying ops infrastructure in software, data & ML (e.g. ETL data pipelines) for continuously processing and training large data sets (e.g. Spark, Flink, Presto).
- Established code and design review feedback to optimize code and design, and defines and encourages high standards and best practices for code and design reviews.
- Experience with containerization software such as Kubernetes, Docker, and Mesos. Experience with large distributed systems.
- Developed distributed systems on one of the main cloud providers, like AWS, Azure, and GCP.
How to hit a homerun
- Knowledge of battery state of health, lifetime and valuation models.
- Knowledge of server hardware to scale, such as data center network fundamentals, OS imaging, provisioning & distribution, and configuration deployment.
Reach out to careers@universeenergy.ai for questions, comments and/or feedback before applying.
