Director, Machine Learning Research; Product

Why work at Nebius

Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in‑house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.

Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in‑house AI R&D team.

We are looking for a Director, Machine Learning Research (Product) - a senior, customer‑facing ML research leader who will keep Nebius at the forefront of modern machine learning and translate cutting‑edge advancements into product direction, platform capabilities, and measurable customer success. You will be a machine learning researcher first, with a strong ML and data foundation, operating at the intersection of Product, Engineering, and Strategic Customers.

In this role, you will continuously deep dive into the evolution of state‑of‑the‑art ML pipelines - including training, inference, fine‑tuning, evaluation, data workflows, and tooling - and convert those insights into clear product requirements and priorities, practical reference architectures for real‑world workloads, and a credible, forward‑looking technical point of view that strengthens Nebius both internally and within the broader ML community.

• Drive forward‑looking ML research to shape Nebius' AI platform and PaaS roadmap, translating frontier developments into clear product direction and priorities.
• Convert state‑of‑the‑art ML pipeline insights into actionable requirements, reference architectures, benchmarks, and gap analyses.
• Partner cross‑functionally with Product, Engineering, and ML teams to align platform capabilities with emerging ML workloads and best‑practice stacks.
• Build strategic collaborations with universities, research labs, and the broader ML ecosystem to accelerate innovation and credibility.
• Establish quality standards for ML-enabled services, including evaluation rigor, reproducibility, reliability, and responsible ML practices.
• Engage strategic customers to understand complex ML scenarios and translate them into clear functional and non‑functional requirements.
• Provide senior technical leadership during evaluations, architecture reviews, and escalations, ensuring customer realities inform platform decisions.
• Articulate and communicate a clear vision for AI‑enabled applications and the infrastructure stack required to support them, influencing both technical and executive audiences.

• 10+ years of experience in machine learning research and/or applied ML (industry, academia, or hybrid), with a strong track record of staying current with the research frontier.
• 5+ years operating as a senior technical leader (Staff/Principal/Director‑level), shaping direction across multiple teams and stakeholders.
• Proven ability to translate research insights into tangible product or platform impact, including requirements, roadmaps, reference architectures, and evaluation standards.
• Experience engaging strategic customers or external partners in deep technical discussions, converting ambiguous goals into clear, actionable requirements.
• Demonstrated collaboration with universities or research labs through joint projects, partnerships, supervision, publications, or advisory roles.
• Strong technical communication record, including internal knowledge‑sharing, external talks, writing, or publications that establish credibility.

• Experience working with ML at scale, including large training runs, high‑throughput inference, or performance‑sensitive pipelines, even if not directly owning the infrastructure.
• Familiarity with modern ML ecosystems and tooling (e.g.,…