Research Scientist (L6) - Machine Learning and Inference Research

Key Skills

Analytical and Problem solving, Design, Effective communication skills, Leadership Skill, Machine learning techniques, Teamwork

Job Description

Netflix is one of the world's leading streaming entertainment services with 238 million paid memberships in over 190 countries enjoying TV series, documentaries, feature films and mobile games across a wide variety of genres and languages. Members can play, pause and resume watching as they want, anytime, anywhere, and can change their plans at any time. As Netflix continues to grow, we are venturing further into exciting new innovations in personalization, discovery, experimentation, backend operations, and more, all driven by research at the frontiers of ML.

The Machine Learning and Inference Research team is an applied research team designed to build up Netflix’s technical capabilities by diving deep and exploring research questions aligned with the biggest challenges at Netflix while partnering with teams to bring these capabilities to life. As a member of the Machine Learning and Inference Research team, you will have the opportunity to drive and execute bleeding-edge research while working in close partnership both with other researchers in the team and with partners in a wide range of product teams. You will leverage your expertise in the frontiers of ML to influence the direction of company roadmaps and work cross-functionally with engineering and product to prototype, test, and implement your research to deliver improvements to the member experience. Additionally, you will be actively engaged with the broader ML research community through conferences, publications, organizational activities, and academic collaborations.

We are looking for an experienced researcher with broad expertise in machine learning and in particular in reinforcement learning and related who can establish and execute a strong research agenda, disseminate knowledge, get others behind their ideas, and foster an open environment of collaboration, innovation, and intellectual excitement.

What you bring

• Ph.D. in a relevant area, with a minimum of 6 years of post-Ph.D. experience across industry and academia.
• Ample ML research experience, in academia and/or industry, including on topics related to sequential decision-making.
• A track record of bringing clarity to and leading ambitious research projects inspired by real-world challenges
• Demonstrates mastery of your technical area through extensive experience evaluating and introducing new methods - either by developing them or keeping pace with industry developments
• Regarded as a role model both because of your technical expertise as well as the impact you drive
• Excellent judgment in identifying and framing ambiguous opportunities and is consistently sought out by stakeholders for guidance, problem-solving, and decision-making
• Passion for collaboration and building solid relationships with colleagues and stakeholders to tackle big, cross-functional problems
• Exceptional communication with a wide variety of technical and non-technical stakeholders 
• Self-motivated to build, with an ability to thrive with minimal oversight and process
• Uplevels the greater org through sharing knowledge and guiding thought on the adoption of new ML methodologies. Actively mentors others and is sought out as a mentor

To learn more about our work, visit our research page.

At Netflix, we carefully consider a wide range of compensation factors to determine your personal top of market. We rely on market indicators to determine compensation and consider your specific job, skills, and experience to get it right. These considerations can cause your compensation to vary and will also be dependent on your location. 

The overall market range for roles in this area of Netflix is typically $390,000 - $900,000. 

This market range is based on total compensation (vs. only base salary), which is in line with our compensation philosophy. Netflix is a unique culture and environment. Learn more here.