Powertrain Software Engineer

Chelmsley Wood

9 months ago

Applications closed

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

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

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

Feb 10, 2026

Careers

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

Jan 23, 2026

Education

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.