Director, Machine Learning Research (Product)

The role 

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.

 

This role is US- remote.

Your responsibilities will include: 

• 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.  

We expect you to have: 

• 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. 

It will be an added bonus if you have: 

• 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., PyTorch and distributed training/serving stacks) and their application in real-world production workflows.
• Experience defining, implementing, or improving evaluation practices, including benchmarking, model quality metrics, offline and online evaluation, and reproducibility standards.
• Prior experience in a field-facing technical leadership role (e.g., Field CTO, Principal Architect, Technical Advisor), partnering closely with GTM and Product teams.

What we expect you to have (skills & traits)

• ML researcher mindset: ability to evaluate new ideas critically, reproduce/validate claims, and separate signal from hype.
• Strong foundation in machine learning and data (modeling, training dynamics, evaluation, dataset considerations, experimentation practices).
• Ability to translate deep technical concepts into clear product implications and customer-ready guidance.
• Comfort operating in a highly cross-functional environment and influencing without direct ownership.
• Strong written and verbal communication: technical docs, product-facing narratives, workshops, and executive-level discussions.

• We offer competitive salaries, ranging from $200k- $300k base equity + quarterly performance bonuses.