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Machine Learning Engineering Manager – Core Ads Ranking

AI Summary
Reddit is hiring a Machine Learning Engineering Manager to lead the Core Ads Ranking team and shape its foundational modeling and user-understanding roadmap. The role combines technical leadership in graph learning, sequence modeling, model calibration, knowledge distillation, and large-scale ML deployment with people management responsibilities. The manager will partner closely with ads retrieval, shopping, click prediction, video, conversion, and platform teams to align strategy and execution. Candidates need management experience, deep hands-on TensorFlow or PyTorch expertise, and a strong record of building and scaling production machine-learning systems. This is a senior US-remote leadership position with a disclosed base-pay range and potential equity and commission eligibility.
Remote from
USA
Salary
USD 230k–322k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
5 Dec 2026
Listing views
20
Application actions
1
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Opportunity details

About this role.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

The Team

The Core Ads Ranking team advances user representation and graph learning, improves sequence modeling across ads verticals, and builds robust pipelines and state‑of‑the‑art architectures for foundational models. The team also leads core initiatives in knowledge distillation, model calibration, and heuristic improvement.

This role offers a unique opportunity to shape and scale Reddit’s foundational modeling and deep user‑understanding roadmap, while multiplying impact through close collaboration with downstream teams including Retrieval, Shopping, Click Prediction, Video View‑Through Prediction, Conversion Prediction, and App Ads Modeling. This position is ideal for a leader with deep machine learning expertise, strong strategic vision, and a collaborative mindset capable of engaging effectively with both technical and cross‑functional partners.

Responsibilities:

  • Set Technical Vision and Strategy: Define and execute a roadmap for foundational model and user understanding, balancing high-ambuiguity long-term investments with measurable milestones.
  • Collaborate Cross-Functionally: Partner with vertical customer teams, horizontal signal teams, and infrastructure/platform teams to align on direction, strategy, priorities, and constraints.
  • Drive Technical Execution: Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance, reliability, and robustness.
  • Lead and Mentor a High-Performing Team: Recruit, mentor, and retain top ML talent while fostering a culture of growth, collaboration, and technical excellence.

Candidate Profile:

  • 1+ years of management experience leading ML-focused teams
  • 6+ years of experience building and scaling machine learning models, and working with state of the art machine learning models
  • 4+ years of hands-on experience with TensorFlow or PyTorch
  • End-to-End ML Lifecycle Experience: Proven ability in training, evaluating, and deploying large-scale models
  • Experience working with foundational models, user or content understanding.
  • Experience with Ads is a plus.

Benefits:

  • Comprehensive Healthcare Benefits and Income Replacement Programs
  • 401k with Employer Match
  • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
  • Family Planning Support
  • Gender-Affirming Care
  • Mental Health & Coaching Benefits
  • Flexible Vacation & Paid Volunteer Time Off
  • Generous Paid Parental Leave

#LI-AS1

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$230,000—$322,000 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightРоль требует одновременно определять долгосрочную ML-стратегию, руководить сильной инженерной командой и обеспечивать надежную поставку моделей в высокомасштабной рекламной среде. Необходимы глубокая экспертиза в современных ML-моделях и уверенное взаимодействие с многочисленными техническими и бизнес-партнерами.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$276,000
US market range (per year)$230k–$340k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Указанный работодателем базовый диапазон составляет $230,000–$322,000 USD в год; годовой период выведен из уровня штатной руководящей ML-роли в США, так как в тексте он не назван напрямую. Оценочный рыночный диапазон для удаленного ML Engineering Manager в США составляет $230,000–$340,000 USD в год без учета акций, комиссии и льгот.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you create a technical roadmap for foundational ads-ranking models amid substantial uncertainty?

I would begin with measurable business and model-quality outcomes, establish a baseline and key constraints, then organize initiatives into near-term experiments and longer-term platform bets. I would use explicit milestones, offline and online evaluation criteria, and regular cross-functional reviews to rebalance investments as evidence changes.

Describe your approach to improving user representations for a large-scale recommendation or ads-ranking system.

I would assess available behavioral, content, and graph signals; define privacy-safe features; and compare representation-learning approaches using offline metrics such as calibration, ranking quality, coverage, and robustness. Promising models would then be validated through controlled online experiments, with monitoring for latency, drift, and segment-level performance.

How do you balance hands-on technical depth with people management responsibilities?

I stay close to architecture, experiment design, risk reviews, and technical decision-making without becoming a bottleneck for implementation. I delegate clear ownership, set high engineering standards, invest in mentoring, and use regular one-on-ones and planning forums to unblock the team and develop future leaders.

What practices would you use to make ML models reliable after deployment?

I would require reproducible training pipelines, data-quality validation, model and feature versioning, pre-launch evaluation gates, and rollback plans. In production, I would monitor service health, latency, prediction distributions, calibration, business metrics, and data drift, with defined ownership and incident-response processes.

How would you resolve disagreement between downstream ads teams and a central modeling team about priorities?

I would make objectives, dependencies, and decision criteria explicit, grounding discussion in expected impact, engineering cost, and platform leverage. Where uncertainty remains, I would propose time-boxed experiments or phased interfaces so teams can generate evidence while maintaining alignment on shared business outcomes.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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