Algorithm Engineer

ROLE:
Senior Battery Algorithm Engineer (Senior to Principal Considered)

LOCATION:
Oxfordshire

COMPENSATION:
Market Leading

TECHNE is supporting an advanced Battery Intelligence technology company in the search for a Senior Battery Algorithm Engineer to join their growing engineering team in Oxfordshire.

This role sits at the forefront of next-generation Battery Management Systems, focused on developing advanced embedded algorithms that directly impact battery safety, fast charging performance, degradation detection, lifetime optimisation, and real-world reliability.

You will work on highly complex nonlinear systems across temperature, ageing, and operational variability, translating advanced theory into embedded, production‑ready solutions.

• Develop advanced battery state estimation and control algorithms across multiple chemistries and operating conditions
• Design diagnostics and prognostics algorithms for next-generation BMS platforms
• Build robust observer architectures using EKF, UKF, Kalman Filters, MPC, and probabilistic estimation techniques
• Deploy real‑time embedded algorithm solutions
• Lead simulation‑based validation activities using representative drive cycles and ageing scenarios
• Analyse cell, module, and pack‑level datasets to identify performance limitations and edge cases
• Support algorithm validation through cell testing and data interpretation
• Collaborate with modelling, validation, embedded software, and systems engineering teams
• Produce technical documentation covering validation, assumptions, and performance metrics

• Degree in Mathematics, Physics, Electrical Engineering, Mechanical Engineering, Statistics, Computer Science, or related STEM discipline
• Experience developing estimation or control algorithms using EKF/UKF, Kalman Filters, MPC, or similar approaches
• Strong understanding of nonlinear systems and estimation theory
• Strong analytical and problem‑solving capabilities
• Experience working within multidisciplinary engineering environments
• Battery algorithms including SOC, SOH, and SOP estimation
• Physics‑based battery modelling including DFN or SPM
• MATLAB/Simulink and model‑based development
• PyBaMM, COMSOL, or similar battery modelling platforms
• Embedded systems and embedded software deployment
• Data‑driven modelling, Gaussian Processes, or embedded ML
• Battery testing and validation
• ASPICE and CI/CD environments such as Git Hub, Git Lab, or Azure Dev Ops

This is an opportunity to join a high‑performing engineering environment developing advanced battery intelligence technology with direct impact on future mobility and energy systems.