Optimization Engineer

ABOUT DEEPFORM

DeepForm Ltd is transforming sheet metal manufacturing with our patented fold-shear pressing process, enabling up to 90% material utilisation and significantly reducing waste, cost and embodied CO2 emissions in the high-volume manufacturing of sheet metal components — without disrupting our customers' existing production lines.

We're a University of Cambridge spin-out, founded in 2022. Having secured external investment and formed a strategic alliance with a well-known automotive manufacturer, we're now delivering our first large-scale project with that partner, and the team has grown accordingly. The technology is proven and now in production with our partner — this is a pivotal stage to join us, with real scope to shape how we scale it, on our mission to reduce waste and embodied emissions in the manufacturing sector.

THE ROLE

As our Optimisation Engineer, you'll recognise and extract key features of component geometries, parameterise forming simulation, apply suitable optimisation techniques to improve the quality of solutions, and develop approaches to digital workflow automation. From time to time you'll also contribute directly to specific projects using DeepForm's patented design methodology to design sheet pressing process layouts that maximise material utilisation, with a likely focus on components used in vehicle bodies.

Key responsibilities:

- Recognising and extracting key features of target products (~10% of your time): identifying geometrical features following surface segmentation and other critical process parameters based on our knowledge base and customer needs

- Parameterisation of process simulation inputs and quality metrics (~20%): generating parameterised FEA models and measuring simulation output performance

- Evaluating and applying optimisation techniques to generate optimal process designs (~35%): identifying and comparing data-based optimisation approaches, combining FEA results and expert input to optimise tool designs

- Developing approaches to digital workflow automation (~35%): linking existing and new digital processes to minimise human intervention, and identifying areas for streamlining and re-structuring to improve performance

WHAT WE'RE LOOKING FOR

Essential:

- A degree in Engineering, Computer Science, Physics or Mathematics, with experience in machine learning or optimisation

- Hands-on experience with data-based engineering optimisation approaches, such as Bayesian optimisation and dimensional reduction, and the ability to apply mathematical optimisation to real manufacturing production design problems

- The ability to develop a software platform that links, automates and optimises existing and new digital workflows

- Strong time management, communication and project management skills — you deliver on time and within budget, and collaborate confidently with your team and external partners

- Independent and self-starting, but happy to consult when key choices are to be made; flexible and comfortable working in a fast-growing team with evolving processes

Desirable:

- Understanding of discrete geometry representation and common geometry characterisation techniques, ideally with prior experience in solid mechanics and FEA simulation

SALARY AND BENEFITS

- Salary: £40,000–£60,000 per year, dependent on experience

- Share option scheme

- Pension scheme

- 25 days' annual leave, excluding bank holidays

- Full time, office-based with some flexibility for hybrid working

Location: Allia Future Business Centre, King's Hedges Road, Cambridge, CB4 2HY, United Kingdom

Reports to: Head of Software

Start date: As soon as possible

OUR HIRING PROCESS

1. Application review

2. Initial screening call with our hiring team

3. Technical interview / short exercise

4. Final interview with the team

5. Offer

We aim to keep the process straightforward and to move quickly once we've found the right person. Applications are reviewed on a rolling basis and this vacancy may close early once a suitable candidate is found.

APPLICANT PRIVACY

DeepForm Ltd will process the personal data you provide as part of your application solely for the purposes of this recruitment process, in accordance with UK GDPR. Your data will not be shared with third parties without your consent, other than as required to assess your application. For details on how we handle your data, or to exercise your data rights, please contact us directly.