Shaping a career in machine learning

Case study overview

Before attending university, Emil had more business experience than many graduates. However, he still credits the ELS scheme with giving him ‘foundational’ experiences that have helped him start successful machine learning companies.

The figure it out phase

Emil wasn’t sure if engineering was for him, so he chose to work before going to university “to figure it out”. He joined a fuel cell startup as a technician. “I learnt a lot of skills, including software programming,” he says.

Having lost his grandfather to a hospital-acquired infection, Emil joined another startup that developed microfluidic chips to measure infections in the blood. Working under the VP of engineering, he built the first prototypes of the medical device for clinical trials. “In the gap between school and university I had already experienced the high-tech science into product translation skillset that engineering gives you,” Emil says.

Sponsored studying

While studying engineering at the University of Cambridge, Emil kept working for startups. He became the leader of the solar-powered car team and used his connections to build relationships with corporate partners.

While he was sponsored by Siemens and worked on their R&D projects each summer, he applied to the ELS programme to test out different career directions including the emerging field of machine learning. Emil used the funds and prestige to attend Intel’s machine learning conference and visit autonomous car research centres in Silicon Valley. “I wouldn’t have been able to do any of that without the award and pretty much every
aspect of that trip became a core foundation of what I did next,” he says. In fact, the researchers he met in California became collaborators and gave him the push to pursue a PhD in computational neuroscience and machine learning.

Machine learning entrepreneurship

While he was still completing his PhD at Cambridge, a trip to Stanford University inspired Emil to set up an open innovation lab – Cambridge Applied Research – for people to collaboratively develop new applications of
machine learning in a range of advanced industries, ranging from new energy and climate technologies to solutions for health and transport systems. From 2013 to date, more than 50 projects have been incubated by Cambridge Applied Research and over 15 exist as independent companies today. 

Based on his own research in machine learning systems, Emil founded Alchera Technologies in 2014, which provides real-time data about how people and vehicles use smart cities. A year later, he co-founded a startup which helps optimise cancer treatments through genomics, cloud-based bioinformatics and machine learning.

Advice

Emil believes the ELS programme offers opportunities both to students chasing a career that’s “a bit different”, as well as those who want to work for large engineering companies. “If you’re keen to think about where engineering could take you, there are not many other opportunities that will directly affect you, your  career and the capabilities you could end up with,” he says