Compensation: £50,000 salary + equity options.
Contract: Permanent, full-time.
Work location: In-person, Cambridge (~10-minute walk from the train station).
Start date: Flexible for the right candidate, ideally within the next 3 months.
Computational fluid dynamics (CFD) simulations are increasingly in demand by engineers, and traditional tools have struggled to keep pace with the growing computational requirements. At Vanellus, we are disrupting the limitations of legacy methods by extending cutting-edge machine learning research to develop radically more efficient CFD. We are helping engineers in a wide range of industries overcome critical challenges posed by expensive CFD, and we already have a waitlist of 40 engineers (including from 7 Formula 1 teams) and have an ML expert working with the America’s Cup sailing competition as an advisor.
We have just received our pre-seed funding to continue the development of our accelerated CFD prototype. Currently, the team comprises the company founders, Laurence Cullen (Machine Learning engineer with 6 years of industry experience in computer vision and NLP) and Dr. Michael Negus (applied maths PhD from Oxford with research focussing on mathematical modelling and CFD).
We are looking for a candidate with a strong mathematical background to help us develop our core CFD solver. You will be working directly alongside the founders to develop a new fluid solver from the ground up using modern numerical programming tools. As our first hire, we expect you to leverage your technical expertise to take an active role in guiding our product development.
As an early-stage startup, we expect your responsibilities will evolve based on the company’s development and your career ambitions. However, in the short term, we expect your responsibilities to include:
You should have: