Research Scientist 6 - Machine Learning and Inference Research, AI Alignment Science

Netflix
Amsterdam, Netherlands
25 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Machine Learning Reinforcement Learning Multi-Agent Systems

Job description

We are seeking an experienced researcher who can establish and execute a strong research agenda in AI alignment science, pursue both internal and external impact, disseminate knowledge effectively and inspire others, collaborate with colleagues to deliver tangible business value, and help foster an open environment of innovation, intellectual rigor, and curiosity. This role will pursue frontier, foundational research that shapes the direction of the field and influences how Netflix develops and applies advanced AI systems across internal workflows and member-facing products.

Requirements

  • Track record of research excellence in the post-training and alignment of foundation models and agentic systems, with expertise in areas such as reinforcement learning, preference optimization and reward modeling, reasoning and test-time inference, model distillation, agent scaffolding and evaluation, multi-agent systems, memory and long-horizon interaction, calibration and uncertainty, interpretability, robustness, or safety.
  • Ph.D. in a relevant area, with at least 2 years of post-Ph.D. experience in industry and/or academia.
  • Experience in applying research (especially your own) to transform real-world problems in collaboration with engineering and business teams.
  • Extensive research experience, in industry and/or academia, including as evidenced in top-tier publications.
  • Recognized for both technical expertise and the impact you drive, and trusted by stakeholders for collaboration, guidance, problem-solving, and decision-making.
  • Excellent judgment in identifying and framing ambiguous research and business problems and the links between the two.
  • Demonstrated ability to collaborate and build strong working relationships with colleagues and stakeholders to tackle big, cross-functional problems.
  • Effective communication with technical, non-technical, and mixed audiences.
  • Able to operate autonomously, take ownership in environments with minimal oversight, and lead work effectively with lightweight processes.
  • Uplevels the greater org through sharing knowledge and guiding thought on the adoption of new methods. Actively mentors others and is sought out as a mentor.

About the company

Netflix is one of the world’s leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

As Netflix grows, we keep advancing innovations in personalization and discovery, experimentation and decision-making, understanding our members and our titles, and backend infrastructure. These developments constantly create new opportunities for research to drive meaningful impact. By exploring the frontiers of AI/ML and intersecting fields, the Machine Learning and Inference Research team turns these opportunities into tangible benefits for our members and our business.

The Machine Learning and Inference Research team is a dedicated research team building up Netflix’s technical capabilities by tackling fundamental research questions tied to our most important challenges and partnering closely with teams across the business to translate research into impact at scale. As a member of the team, you will leverage your technical expertise to shape roadmaps, collaborate across functions, and bring new ideas from exploration to impact. You will also engage actively with the broader research community by publishing at top venues, presenting at conferences, mentoring interns, and fostering academic collaborations.

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