[All remote jobs](https://jobicy.com/jobs.md)[![Reddit logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/10/Reddit.jpg)](https://jobicy.com/company/reddit.md)[Reddit](https://jobicy.com/company/reddit.md)

# Machine Learning Manager, Feed Relevance (Retrieval)

AI Summary Reddit seeks an experienced engineering manager to lead the Feed Retrieval machine learning team. The team develops large-scale candidate retrieval and recommendation systems that power personalized feeds for more than 120 million daily users. The manager will set technical strategy, prioritize a roadmap, coach ML engineers, and ensure reliable, low-latency, cost-effective production systems. The role requires deep expertise in recommender systems, embeddings, retrieval, experimentation, and cross-functional technical leadership.[Apply for this job](#job-application)[View company](https://jobicy.com/company/reddit.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 253,300–354,600 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished5 Nov 2026Apply before5 Dec 2026Listing views37Application actions1Application toolkit

## Make your next move.

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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](http://www.redditinc.com/).

Reddit is looking for an experienced Engineering Manager to lead our Feed Retrieval team. In this role, you’ll lead a high-impact team of Machine Learning Engineers building the systems that identify, retrieve, and shape the candidate inventory powering Reddit’s personalized feeds. Your team will work at the foundation of Feed Relevance: expanding the set of high-quality content Reddit can recommend, improving personalization and discovery for users across different levels of signal, and building scalable ML systems that directly shape the experiences of over 120M+ daily users.

If applying ML / AI in production to improve Reddit Relevance excites you, then you’ve found the right place.

Responsibilities:

* Define Technical Vision & Strategy: Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives.
* Roadmap & Prioritization: Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability.
* Team Leadership & Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact.
* Technical Execution & Delivery: Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.
* Measurement & Learning: Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact.
* Platform & Infrastructure Collaboration: Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations.
* Operational Excellence: Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices.
* Cross-Functional Partnership: Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work.
* Recruiting & Growth: Partner with our incredible recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.

Qualifications:

* Experience Leading ML Teams: 2+ years of experience building and managing high-performing ML or recommender-systems teams.
* Deep ML Expertise: Hands-on experience with large-scale production ML systems, ideally including recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-powered recommendation applications.
* Technical Domain Knowledge: Strong understanding of recommender systems, especially candidate retrieval, embedding/indexing systems, ranking handoffs, feed personalization, exploration, content quality, and measurement strategies.
* Strategic Thinking: Ability to develop and communicate a clear technical strategy across ambiguous problem spaces, balancing user relevance, ecosystem health, system scalability, and business impact.
* Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.
* Exceptional Communication & Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners.

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-remote, #LI-JS5

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/](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:

$253,300—$354,600 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](https://redditinc.com/policies/candidate-privacy-policy).

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.

Show more

[Apply now >](https://jobicy.com/jobs/159080-machine-learning-manager-feed-relevance-retrieval.md)

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## Role DNA

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

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightЭто высокоуровневая роль, совмещающая управление командой с ответственностью за стратегию и эксплуатацию масштабных ML-систем рекомендаций. Нужно принимать сложные компромиссы между качеством моделей, задержками, стоимостью, безопасностью и продуктовым воздействием.

## Salary analysis

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

Estimated job median (per year)Market rate$303,950US market range (per year)$250k–$380k0$418.00k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Работодатель указывает базовый годовой оклад $253,300–$354,600 USD; середина диапазона составляет $303,950 в год. Оценочный рыночный диапазон для руководителя ML-направления рекомендательных систем в США — $250,000–$380,000 в год без учета акций, комиссий и льгот. Годовой период для базовой зарплаты логически выведен из масштаба указанной суммы и контекста штатной руководящей роли в США.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Machine Learning Leadership](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Leadership.md)[Recommender Systems](https://jobicy.com/jobs?search_keywords=Recommender%20Systems.md)[Candidate Retrieval](https://jobicy.com/jobs?search_keywords=Candidate%20Retrieval.md)[Personalization](https://jobicy.com/jobs?search_keywords=Personalization.md)[Embedding Models](https://jobicy.com/jobs?search_keywords=Embedding%20Models.md)[Vector Indexing](https://jobicy.com/jobs?search_keywords=Vector%20Indexing.md)[Ranking Systems](https://jobicy.com/jobs?search_keywords=Ranking%20Systems.md)[Experiment Design](https://jobicy.com/jobs?search_keywords=Experiment%20Design.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[ML Infrastructure](https://jobicy.com/jobs?search_keywords=ML%20Infrastructure.md)[Low-Latency Systems](https://jobicy.com/jobs?search_keywords=Low-Latency%20Systems.md)[Technical Strategy](https://jobicy.com/jobs?search_keywords=Technical%20Strategy.md)[People Management](https://jobicy.com/jobs?search_keywords=People%20Management.md)[Cross-Functional Collaboration](https://jobicy.com/jobs?search_keywords=Cross-Functional%20Collaboration.md)

Sample interview questionsHow would you define success for a feed candidate-retrieval system?I would use a balanced metric framework covering candidate coverage, relevance and diversity, freshness, source incrementality, downstream ranking and engagement impact, latency, reliability, and infrastructure cost. I would validate improvements through controlled experiments and guardrail metrics for ecosystem health and content quality.

Describe how you would prioritize retrieval-quality improvements against latency and infrastructure cost.

I would quantify the expected user and business impact of each initiative, establish latency and cost budgets, and test high-potential changes incrementally. Decisions should be based on online experiment results and operational data rather than offline model metrics alone.

How do embedding-based retrieval systems differ from traditional retrieval approaches?

Embedding retrieval represents users and content in a shared vector space, enabling semantic matching and broad candidate discovery. It requires careful work on embedding quality, index freshness, approximate nearest-neighbor tradeoffs, filtering, and handoff to downstream ranking.

How would you develop an ML engineering team working on ambiguous recommendation problems?

I would set a clear strategy and measurable outcomes, give engineers ownership of meaningful problem areas, and create regular design, experiment, and incident-review practices. Coaching would focus on both technical depth and the ability to communicate tradeoffs to partners.

How would you investigate a decline in feed relevance after a retrieval-system launch?

I would first verify instrumentation and isolate the affected cohorts, traffic sources, and candidate sources. Then I would compare candidate coverage, retrieval scores, latency, freshness, ranking handoff behavior, and online metrics against a control, rolling back or gating the change if guardrails are breached.

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