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# Machine Learning Scientist 6 - Ads Member

- **Company:** Netflix, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Content Analysis, Data Infrastructure, Python (Programming Language), Operational Databases, Large Language Models, Data Strategy, Usage Tracking, Information Technology, Machine Learning Operations, Feature Extraction, User Identification
- **Published:** October 13, 2026
- **Apply:** https://startup.jobs/machine-learning-scientist-6-ads-member-understanding-netflix-com-10432610

## About the Role

* Advanced degree (PhD or Master's) in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent industry experience.
 * 7+ years of industry experience building and shipping production ML systems at scale, with demonstrated staff/senior-staff-level scope and impact.
 * Track record building user or audience understanding systems - embeddings, representation learning, identity resolution, or similar - ideally in a monetization or ads context.
 * Hands-on experience applying LLM to user modeling, content understanding, or recommendation, with good judgment on where these approaches beat traditional methods.
 * Influence 1P/3P data strategy, privacy-aware data usage, or identity/targeting infrastructure is a strong plus.
 * A track record of setting technical direction and influencing roadmap across teams; experience as a vertical or xfn technical lead is a strong plus.
 * Proficiency in Python, Scala, or Java, and experience prototyping and deploying models on large-scale production data.
 * Strong business acumen and the ability to translate technical results into business impact.
 * Excellent communication and cross-functional collaboration skills.

## Description

* Set the technical direction for user understanding by leveraging multimodal signals - from content, viewing behavior, and context.
 * Shape and land Netflix's data strategy for a fast-growing team, partnering with privacy, legal, and platform engineering to build a data foundation that scales.
 * Partner with the ranking, bidding & pacing teams to get user-understanding signals into production and drive downstream impact.
 * Build rigorous online and offline evaluation frameworks to quantify the incremental value of new signals, representations, and models.
 * Communicate technical strategy, trade-offs, and results to both technical and non-technical audiences, including senior leadership