Lead Machine Learning Engineer, Entry and Re-engagement
Paramount Skydance Corporation - New York, NY
Posted Jun 12, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
- Not verified
- Verification
- Not verified
- Salary
- Not verified
- 401(k) match
- Not verified
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Market context
- U.S. role benchmark (BLS OEWS)
- $111,944 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Lead Machine Learning Engineer, Entry and Re-engagement New York, NY #WeAreParamount on a mission to unleash the power of content… you in? We've got the brands, we've got the stars, we've got the power to achieve our mission to entertain the planet - now all we're missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture. Senior Lead Machine Learning Engineer, Entry & Re-engagement 45724 Overview We are looking for a Senior Lead Machine Learning Engineer for the Entry pod. This team owns the high stakes surfaces that establish whether a user stays or bounces within seconds of opening the app. You will lead the ML strategy for Onboarding, Re-entry, and High-Commitment Recommendations, including core features like "Your Next Watch", "Jump Back In", and "While You Were Away". The technical task of this pod is optimizing start rates under high uncertainty. You will design systems that oversee the "cold start" problem for new users and the "intent gap" for returning users. Because these surfaces share similar failure modes such as over-indexing on recency or failing to surface "must-watch" content. You will architect an unified approach to re-engagement that balances historical preference with real-time context. The Entry pod owns the "Moment of Truth." In this
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