Algorithm Engineer; m​/f​/d

The world talks about batteries, cells and the energy transition. Almost nobody talks about the Battery Management System (BMS) that makes them work.

As battery applications multiply, from robotics and mobility to data centers and grid-scale storage, BMS complexity is exploding. Legacy BMS architectures were never built for this: too rigid to keep up with new requirements, they've turned the BMS into the bottleneck of battery development.

We're building something different: a novel software-defined BMS architecture combined with an AI-driven configuration process. It gets battery manufacturers the right BMS for their exact use case within days, regardless of application or cell chemistry.

We're building the battery management system platform of tomorrow. Delivered in days, not years.

Join us and shape one of the fastest growing markets in the energy sector:
The BMS market.

At Anori Tech, we believe everything is energy, and progress runs on it. With Anori we actively shape the energy transition: from oil to electric, from low to high efficiency. We need batteries for this transition and almost all batteries need a BMS. We strive for Shenzhen speed and German engineering quality. Our objective: put Europe back on the map in the race for the energy transition and build technical leadership out of Hamburg.

A BMS is only as good as what it knows about the battery. As our Algorithm Engineer, you'll develop the algorithms that tell the Anori BMS what is happening inside the cells: state of charge, state of health and balancing. And you won't stop at the concept: you'll design algorithms, implement them in our embedded Rust firmware and validate them on real hardware.

Later on, the role grows into the cloud, where data pipelines and fleet analytics will build on the models you create today. You'll work closely with Stefan, our CTO, and our firmware and hardware engineers.

Start date
: from January 1, 2027

• Develop the algorithms that estimate SoC and SoH for different cell chemistries and applications

• Design balancing strategies that fit the hardware and use case

• Build battery models (e.g. equivalent circuit models) and parameterize them from measurement data with statistical rigor

• Implement your algorithms directly in the embedded Rust firmware, efficient enough for constrained hardware

• Design experiments and evaluation methodologies: analyze failure modes, quantify trade-offs between accuracy, robustness and compute cost

• Support in building data pipelines and fleet analytics in the cloud

• Hands-on experience with state of charge and state of health estimation, shown in a product, publication or substantial project. For example Kalman-type filters, H∞ filters, observers, or coulomb counting with model-based correction

• Deep understanding of battery behavior and modeling, e.g. equivalent circuit models, hysterisis, ageing or also physics based models

• Experience implementing numerical algorithms on embedded, constrained hardware (e.g. in Rust or C/C++ - heads-up our firmware is written in Rust, and we'll help you get up to speed)

• Strong Python skills for modeling, simulation and data analysis

• Critical, analytical, systematic thinking: you reason from first principles, make your assumptions explicit and challenge existing paradigms

• Experience with cell balancing algorithms and their effects

• Working with real cell data from test benches or cyclers

• Signal processing fundamentals: FIR/IIR filter design, sampling and aliasing, noise characterization of sensor data

• Data pipelines, fleet analytics or cloud experience

• Machine learning methods for battery data, for example data-driven state of health…