Senior Machine Learning Engineer at Reddit

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Senior Machine Learning Engineer at Reddit · Katori

Asks for

The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals”
The ad says“Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals”
The ad says“Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals”
The ad says“Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”
The ad says“ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)”

Values

The ad says“Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”
The ad says“Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”
The ad says“Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”
The ad says“Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale”
The ad says“Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems”
The ad says“Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale”
The ad says“Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems”
The ad says“Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”
The ad says“Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”
The ad says“Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems”
The ad says“Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)”

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.

At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning.

We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem.

We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit.

If you love working on complex, real-world ML problems at massive scale, this role is for you.

What You’ll Work On

As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including:

Personalized recommendations, search, and ranking systems that help users discover the most relevant content and communities
Intelligent advertising systems including ranking, bidding, measurement, and optimization
Content, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signals
Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems
Applied AI and LLM-driven experiences that improve relevance, discovery, and user engagement

You’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes.

What You’ll Do

Design, build, and deploy production-grade machine learning models and systems at scale
Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
Build scalable data and model pipelines with strong reliability, observability, and automated retraining
Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.
Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
Improve system performance across latency, throughput, and model quality metrics
Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment
Contribute to technical strategy, architecture, and long-term ML roadmap

Basic Qualifications

3-5+ years of experience building, deploying, and operating machine learning systems in production
Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
Experience improving measurable metrics through applied machine learning

Preferred Qualifications

Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
Experience working with real-time systems and low-latency production environments
Background in feature engineering, model optimization, and production monitoring
Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams

Ads Measurement Modeling
Ads Targeting and Retrieval
Advertiser Optimization
Ads Marketplace Quality
Ads Creative Effectiveness
Ads Foundational Representations
Ads Content Understanding
Ads Ranking
Feed Relevance
Search and Answers Relevance
ML Understanding
Notifications Relevance

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

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.