Director, Machine Learning Research (Product)

United StatesRemoteexecutive

Data ScienceDevOps & InfrastructureMachine LearningData & AI

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Quick Summary

Key Responsibilities

Drive forward-looking ML research to shape Nebius’ AI platform and PaaS roadmap, translating frontier developments into clear product direction and priorities.

Requirements Summary

Drive forward-looking ML research to shape Nebius’ AI platform and PaaS roadmap, translating frontier developments into clear product direction and priorities.

Technical Tools

Data ScienceDevOps & InfrastructureMachine LearningData & AI

Requirements

~1 min read

Responsibilities

~1 min read

→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.

Nice to Have

~1 min read

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.

Requirements

~1 min read

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.

Compensation