In early 2026, the judging panel of the Artificial Intelligence Excellence Awards included the name of Maksim Lykov — an engineer based in Meta’s London office. The awards’ organizer, Business Intelligence Group, builds its judging panels from practicing industry specialists.
Like many self-taught engineers, Maksim had no formal technical education. He taught himself programming, preparing for interviews without a single line of commercial experience on his resume. He landed his first developer job without a single project in his portfolio and without any prior employment history.
His career then progressed step by step: Yandex, then Google, then Meta — three companies, in each of which he was responsible for systems that tens of millions of people depend on.
Streamlining account recovery and preventing repeat break-ins
At Meta, Maksim works on the backend infrastructure that protects accounts and restores access to them across web and mobile platforms. His team owns the flow that runs every time a user tries to regain access to a compromised Facebook account.
Maksim redesigned key parts of this recovery flow. It now handles tens of millions of recovery attempts every month. Since the changes, the share of successful recoveries has grown by 7%, while the rate of repeat break-ins on the same account has dropped by 1.1 percentage points.
At the same time, he works on a separate track — infrastructure and tools for evaluating and benchmarking AI models inside the company. Both projects tackle the same question from different angles: how to verify that a system actually works as intended, at a scale that is difficult to even picture.
Building ML infrastructure
At Yandex, Maksim contributed to building shared ML infrastructure — Feature Store and Long Term Profiles. The project consolidated more than 100 features that had previously been used separately across different machine learning teams, and it became a unified base for advertising systems serving an audience of roughly 77 million users per month.
Solving Android Auto’s problems
At Google, Maksim was responsible for the reliability of Android Auto: he built a stress-testing system and automation tools. As a result, the number of launch errors dropped by 36%, and the project received Google’s internal Auto Awards. Separately, he worked on the connectivity side of Android Auto together with automotive partners:
- Nissan
- BMW
- Polestar
- Volvo
- MINI
Six awards programs in a year and a half
Business Intelligence Group is a US organization that runs several industry awards programs and staffs its judging panels with practicing experts from the field. Between 2025 and 2026, Maksim joined the judging panel for six such programs.
Beyond certificates, he has a public judge profile on the organization’s website — with a photo, his employer, and a link to his LinkedIn. Among the nominations he evaluated were products in the categories of automotive AI and supply chain technology.
Judging programs:
- The Sammy — Sales and Marketing Awards (2025)
- Sustainability Awards (2025)
- Stratus Awards for Cloud Computing (2025)
- Artificial Intelligence Excellence Awards (2026)
- Excellence in Customer Service Awards (2026)
- Evan Kirstel’s We Love Tech Awards (2026)
Talks at Geekle, Conf42, and System.Design
Over the past two years, Maksim has spoken at several industry conferences: the Geekle Worldwide Software Architecture Summit Winter ’24, Conf42 IoT, System.Design — where he gave a talk on trade-offs between quality attributes, funding, and timelines in system design — as well as the Software Architecture Conference and ProductMindset.
Going forward, he plans to move further into AI infrastructure and high-load backend systems, helping build the platforms and tools the industry will need over the next few years.
How to influence business outcomes without a management title
Throughout his career, Maksim has worked as an individual contributor rather than in management. Even so, his work has directly influenced systems used by tens of millions of people: from improving Android Auto reliability, to building ML infrastructure, to increasing successful account recoveries at Meta.
Systems thinking and measurable results — that’s how Maksim himself describes the principle behind each of these projects. It’s telling that he now applies the same principle to evaluating other people’s work.
