Antoni Shtipliyski offers advice on how early-career researchers can transition into machine-learning roles in industry.
In the past decade, machine learning has surged into every corner of industry, from travel and transport to healthcare and finance. For early-career researchers, who have spent their PhDs and postdocs coding, a job in machine learning may seem a natural next step.
“Scientists often study nature by attempting to model the world around us into mathematical models and computer code,” says Antoni Shtipliyski, engineering manager at Skyscanner. “But that’s only one part of the story if the aim is to apply these models to large-scale research questions or business problems. A completely orthogonal set of challenges revolves around how people collaborate to build and operate these systems. That’s where the real work begins.”
Used to large-scale experiments and collaborative problem solving, particle physicists are uniquely well-equipped to step into machine-learning roles. Shtipliyski worked on upgrades for the level-1 trigger system of the CMS experiment at CERN, before leaving to lead the machine-learning operations team in one of the biggest travel companies in the world.
Effective mindset
“At CERN, building an experimental detector is just the first step,” says Shtipliyski. “To be useful, it needs to be operated effectively over a long period of time. That’s exactly the mindset needed in industry.”
During his time as a physicist, Shtipliyski gained multiple skills that continue to help him at work today, but there were also a number of other areas he developed to succeed in machine learning in industry. One critical gap in a physicists’ portfolio, he notes, is that many people interpret machine-learning careers as purely algorithmic development and model training.
“At Skyscanner, my team doesn’t build models directly,” he says. “We look after the platform used to push and serve machine-learning models to our users. We oversee the techno-social machine that delivers these models to travellers. That’s the part people underestimate, and where a lot of the challenges lie.”
An important factor for physicists transitioning out of academia is to understand the entire lifecycle of a machine-learning project. This includes not only developing an algorithm, but deploying it, monitoring its performance, adapting it to changing conditions and ensuring that it serves business or user needs.
Learning to write and communicate yourself is incredibly powerful
“In practice, you often find new ways that machine-learning models surprise you,” says Shtipliyski. “So having flexibility and confidence that the evolved system still works is key. In physics we’re used to big experiments like CMS being designed 20 years before being built. By the time it’s operational, it’s adapted so much from the original spec. It’s no different with machine-learning systems.”
This ability to live with ambiguity and work through evolving systems is one of the strongest foundations physicists can bring. But large complex systems cannot be built alone, so companies will be looking for examples of soft skills: teamwork, collaboration, communication and leadership.
“Most people don’t emphasise these skills, but I found them to be among the most useful,” Shtipliyski says. “Learning to write and communicate yourself is incredibly powerful. Being able to clearly express what you’re doing and why you’re doing it, especially in high-trust environments, makes everything else easier. It’s something I also look for when I do hiring.”
Industry may not offer the same depth of exploration as academia, but it does offer something equally valuable: breadth, variety and a dynamic environment. Work evolves fast, deadlines come more readily and teams are constantly changing.
“In academia, things tend to move more slowly. You’re encouraged to go deep into one specific niche,” says Shtipliyski. “In industry, you often move faster and are sometimes more shallow. But if you can combine the depth of thought from academia with the breadth of experience from industry, that’s a winning combination.”
Applied skills
For physicists eyeing a career in machine learning, the most they can do is to familiarise themselves with tools and practices for building and deploying models. Show that you can use the skills developed in academia and apply them to other environments. This tells recruiters that you have a willingness to learn, and is a simple but effective way of demonstrating commitment to a project from start to finish, beyond your assigned work.
“People coming from physics or mathematics might want to spend more time on implementation,” says Shtipliyski. “Even if you follow a guided walkthrough online, or complete classes on Coursera, going through the whole process of implementing things from scratch teaches you a lot. This puts you in a position to reason about the big picture and shows employers your willingness to stretch yourself, to make trade-offs and to evaluate your work critically.”
A common misconception is that practicing machine learning outside of academia is somehow less rigorous or less meaningful. But in many ways, it can be more demanding.
Scientific development is often driven by arguments of beauty and robustness. In industry, there’s less patience for that,” he says. “You have to apply it to a real-world domain – finance, travel, healthcare. That domain shapes everything: your constraints, your models, even your ethics.”
Shtipliyski emphasises that the technical side of machine learning is only one half of the equation. The other half is organisational: helping teams work together, navigate constraints and build systems that evolve over time. Physicists would benefit from exploring different business domains to understand how machine learning is used in different contexts. For example, GDPR constraints make privacy a critical issue in healthcare and tech. Learning how government funding is distributed throughout each project, as well as understanding how to build a trusting relationship between the funding agencies and the team, is equally important.
“A lot of my day-to-day work is just passing information, helping people build a shared mental model,” he says. “Trust is earned by being vulnerable yourself, which allows others to be vulnerable in turn. Once that happens, you can solve almost any problem.”
Taking the lead
Particle physicists are used to working in high-stakes, international teams, so this collaborative mindset is engrained in their training. But many may not have had the opportunity to lead, manage or take responsibility for an entire project from start to finish.
“In CMS, I did not have a lot of say due to the complexity and scale of the project, but I was able to make meaningful contributions in the validation and running of the detector,” says Shtipliyski. “But what I did not get much exposure to was the end-to-end experience, and that’s something employers really want to see.”
This does not mean you need to be a project manager to gain leadership experience. Early-career researchers have the chance to up-skill when mentoring a newcomer, help improve the team’s workflow in a proactive way, or network with other physicists and think outside the box.
You can be the dedicated expert in the room, even if you’re new. That feels really empowering
“Even if you just shadow an existing project, if you can talk confidently about what was done, why it was done and how it might be done differently – that’s huge.”
Many early-career researchers hesitate prior to leaving academia. They worry about making the “wrong” choice, or being labelled as a “finance person” or “tech person” as soon as they enter another industry. This is something Shtipliyski struggled to reckon with, but eventually realised that such labels do not define you.
“It was tough at CERN trying to anticipate what comes next,” he admits. “I thought that I could only have one first job. What if it’s the wrong one? But once a scientist, always a scientist. You carry your experiences with you.”
Shtipliyski quickly learnt that industry operates under a different set of rules: where everyone comes from a different background, and the levels of expertise differ depending on the person you will speak to next. Having faced intense imposter syndrome at CERN – having shared spaces with world-leading experts – industry offered Shtipliyski a more level playing field.
“In academia, there’s a kind of ladder: the longer you stay, the better you get. In industry, it’s not like that,” says Shtipliyski. “You can be the dedicated expert in the room, even if you’re new. That feels really empowering.”
Industry rewards adaptability as much as expertise. For physicists stepping beyond academia, the challenge is not abandoning their training, but expanding it – learning to navigate ambiguity, communicate clearly and understand the full lifecycle of real-world systems. Harnessing a scientist’s natural curiosity, and demonstrating flexibility, allows the transition to become less about leaving science behind, and more about discovering new ways to apply it.
“You are the collection of your past experiences,” says Shtipliyski. “You have the freedom to shape the future.”