Senior Machine Learning Engineer, Ads Content Understanding

Remote from: USA
Salary, yearly, USD: 216,700 - 303,400
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 14 Jul 2026
Apply before: 14 Aug 2026
Experience level: Senior
Views / Applies: 27 / 2

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About Reddit

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AI Summary

Reddit is hiring a Senior Machine Learning Engineer for the Ads Content Understanding team. This role involves building and optimizing ML systems to understand content, enhance ad targeting, and ensure brand safety. The engineer will work on knowledge graphs, taxonomies, and LLM-powered signals. Responsibilities span the full ML lifecycle, including evaluation, monitoring, and mentorship. The role requires 5+ years of production ML experience and strong engineering skills.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This is a senior role requiring deep expertise in production ML, technical leadership, and cross-functional collaboration, making it a challenging position. However, it is well-defined with clear expectations.

Salary Analysis

AI Insight The offered salary range of $216,700 to $303,400 is highly competitive, exceeding typical market medians for senior ML roles. This reflects Reddit's commitment to attracting top talent and the high impact of the position.

Core Skills Required

Machine Learning Engineering Deep Learning LLM Natural Language Processing Content Understanding Ads Systems Knowledge Graphs Production ML Systems Python PyTorch

Cover Letter Sample

Dear Hiring Manager,

I am thrilled to apply for the Senior Machine Learning Engineer role on the Ads Content Understanding team at Reddit. With over 5 years of experience delivering production ML systems at scale, I have a strong track record in content understanding, NLP, and ad targeting. I am particularly drawn to Reddit's mission to foster authentic conversations and the opportunity to build robust signals that power contextual and shopping ads.

In my previous role at [Company], I led the development of ML pipelines for content classification and entity extraction, achieving significant improvements in ad relevance and brand safety. I have extensive experience with LLMs, including fine-tuning and distillation, which aligns perfectly with the team's focus on LM-judge practices and operationalizing commercial conversations. I am excited about the chance to mentor engineers and drive technical excellence in a fast-paced, collaborative environment.

Thank you for considering my application. I look forward to the possibility of contributing to Reddit's growth and innovation.

Sincerely,
[Your Name]

Sample Interview Questions

Describe your experience building and deploying large-scale ML systems for content understanding. What challenges did you face with latency, accuracy, and cost?

I led a project to classify user-generated content into taxonomies with low latency. We used a two-stage pipeline: a lightweight model for initial filtering and a more accurate LLM for ambiguous cases. Challenges included balancing precision and recall, managing inference costs by distilling the LLM into a smaller model, and implementing monitoring for concept drift. We achieved 95% precision with 4x cost reduction after distillation.

How would you approach evaluating and monitoring the quality of LLM-generated signals in a production setting?

I would implement a multi-faceted evaluation system: offline benchmarks using human-labeled datasets, automated unit tests for edge cases, and online A/B testing to measure business impact. For monitoring, I'd track metrics like coverage, latency, and agreement with human raters. Additionally, I'd use adversarial detection to catch safety issues and set up dashboards for real-time alerting. Regular revalidation against holdout sets would ensure sustained quality.

Explain a time you had to choose between using a hosted LLM API and building a custom model. What factors influenced your decision?

At my last job, we needed to extract product attributes from text. Initially, we used a hosted LLM for its accuracy, but costs escalated with scale. I evaluated factors: latency requirements (we could tolerate ~500ms), data privacy (some data was sensitive), and customization needs (had to handle domain-specific terms). We decided to fine-tune a smaller open-source model on our data, which reduced costs by 80% while maintaining 90% accuracy. The decision was data-driven, involving benchmarking both approaches.

How do you ensure that ML models for ad content understanding are fair and unbiased?

I incorporate fairness checks throughout the pipeline: during data collection, I analyze representation across demographics; during training, I use techniques like re-weighting or adversarial debiasing; and during evaluation, I measure performance on subgroups. I also monitor for disparate impact in production by tracking metrics per segment. For content understanding, it's critical to avoid associating sensitive attributes with certain categories. I advocate for model cards and regular audits.

Describe your experience mentoring other engineers. How do you approach code reviews and technical guidance?

I actively mentor by pairing on complex tasks, hosting workshops on ML best practices, and providing constructive feedback. In code reviews, I focus on both correctness and readability, often explaining the 'why' behind suggestions. I encourage engineers to explore multiple solutions and guide them to make informed trade-offs. For technical guidance, I emphasize documentation, testing, and scalability. I believe in empowering others to own their work while offering support when needed.

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 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you’d like. Don’t live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence.

Ads Content Understanding (ACU) owns and produces signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU is responsible for:

The Knowledge Graph (entities, brands, products, and relationships across Reddit and external sources).
Content taxonomies such as IAB, Shopify Standard Product Taxonomy, IAS, and other commercial taxonomies used for targeting, safety, and marketplace dynamics.
Opinion mining for ads use cases: sentiment, stance, commercial intent, and other qualitative attributes of conversations.
Shopping / product understanding: detecting product entities, product categories, and product attributes in organic conversations and aligning them with shopping catalogs.
Signals and tags registry: a unified, governed catalog of ACU signals that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams.

We are looking for a Senior Machine Learning Engineer (IC4) who will act as a key contributor to the Content Understanding roadmap for the Monetization org.

This is not a research scientist or pure DS role; success is defined by robust, shipped systems and monetization impact. The ideal candidate is a pragmatic engineer with strong software engineering fundamentals and solid ML intuition—not a pure research scientist. This is an Applied MLE role, requiring someone who can evaluate when to leverage hosted LLMs versus custom models, help scale content understanding to new modalities (e.g., video), and drive practical ML solutions that deliver business impact.

Responsibilities:

Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable ML pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.
Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.
Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices.
Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)
Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.
Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.

Required Qualifications:

5+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads.
Demonstrated Senior-level technical leadership: has contributed to architecture decisions, standards, and design reviews in their immediate team
Strong communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization.
Some experience building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.

Preferred Qualifications:

Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.
Significant experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one statically typed language like Go/Java/C++). Comfortable owning training, evaluation, and deployment code end-to-end.
Experience designing ML systems and pipelines: offline training, feature pipelines (batch/streaming), online serving, monitoring, and experimentation for high-traffic surfaces.

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

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:

$216,700—$303,400 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.

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