Powertrain Software Engineer

Chelmsley Wood
4 days ago
Applications closed

Related Jobs

View all jobs

Powertrain Charging Test Data Engineer

Data Analytics Engineer

Data Analytics Engineer

As the Senior/Principal Engineer in the Vehicle Supervisory Management Software team, a major and integral part of your role will be ensuring the successful delivery of Powertrain & Vehicle control software, covering both platform and applications for different Powertrain Architectures (DHT, REEV, BEV).
You will also support designing new control system strategies with smart technologies in topics like Thermal Management, 4WD Torque Split Strategy, Energy Management, Predictive Features etc and leading the team to deliver software based on those.
This role will require you to work with the wider cross-functional teams to develop a good understanding of various powertrain technologies and, to communicate clearly with non-software domain engineers.
Powertrain Software Engineer Role:
Duties & Responsibilities
Prepare Technical Roadmap & Lead New Function Development.
Prepare technical roadmap for the subject area.
Develop and design new functions.
Support SW Architect with ideas and designs to help support move to centralised E/E architectures.
Review and approve work products created by the team.
Ensure architectural integrity of software solutions and act as the technical authority for supervisory controller functions.
Provide ongoing assessment of software design processes and optimise changes as needed.
Contribute to Advanced Research
Support advanced SW development including machine learning and big data features.
Support development and validation of advanced energy management algorithms, Thermal management Strategy and 4WD Torque Split Strategy.
Support software platform architectures design and optimisation.
Supply software to support the developments of smart and zero-carbon powertrain components.
Support and Lead Benchmarking exercises to understand competition.
Collaborate Across Departments
Collaborate with Calibration & Vehicle Integration Team to deliver fully calibrated functions to vehicles.
Collaborate with Simulation teams and develop strategies to improve electrical and thermal efficiencies of vehicles.
Collaborate with HQ teams and coach engineers to define the customer value of technical solutions.
Interface and collaborate with software team lead and colleagues based in China HQ.
Powertrain Software Engineer Requirements:

  • Bachelor’s degree in engineering, computer science, maths or physics.
  • At least 5 years of experience developing and implementing software for Powertrain & Vehicle Supervisory Controllers.
  • Knowledge on topics like Torque Management, Traction Management & Thermal Management.
  • Curious and Perpetual Learning mindset to be able to apply cutting edge technologies like AI/ML to influence Supervisory Management Control Strategies.
  • An individual with a Customer Focussed Mindset who can translate complex technical solutions into real world customer benefits.
  • An individual who is resilient, energetic and enthusiastic, responding constructively to new ideas and changing environments.
  • Experience in designing Safety Functions for Powertrain Supervisory Controllers with particular focus and expertise on topics like Torque Management, Traction Management & Thermal Management.
  • Expert understanding of model-based software development with MATLAB / Simulink.
  • Good working knowledge of software development processes, workflows, and standards e.g., Auto SPICE.
  • Good working knowledge of software version control tools e.g., Git / Subversion / Perforce.
  • Experience of designing and applying formal software architectures.
  • Capable of delivering high-level technical presentations to senior management.
  • Ability to prioritise multiple work streams in a dynamic and changing environment.
  • Occasional travel to other CA sites, on development trips and to visit suppliers is a requirement
    Desirable:
  • Practical knowledge of V-cycle and Agile development methodologies.
  • Experience in delivering mass-production automotive embedded software projects.
  • Experience in software for safety critical systems.
  • Experience in DevOps tool chain and implementation.
  • Experience of software testing and quality metrics (coverage analysis, complexity analysis).
  • Experience with automotive communication protocols e.g. CAN / CAN FD / UDS.
  • Knowledge of machine learning and big data algorithms and implementations.
    Benefits:
    Our Client offers a competitive basic that is open to negotiation, plus a Bonus Scheme, Healthcare, Pension and free Lunches. Relocation is also offered.
    Applications:
    This vacancy is only available to Candidates with relevant experience as detailed in the job description. Due to volume of applications, we are unable to respond to applicants who do not possess the required skills and experience. Recent Graduates who do not have the required level of industry experience need not apply.
    Candidates must be authorised to work in the country where this role is located BEFORE making an application

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Careers Jobs

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Jobs

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.