Reddit is looking for a Machine Learning Engineer to design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, ranking, and advertising. You will own the full ML lifecycle from research and modeling to production deployment at massive scale.
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.
What We're Looking For
- 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.
Nice to Have
- 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.
Technical Stack
- Languages: Python, Java, Go
- ML Frameworks: PyTorch, TensorFlow
- Data & Infrastructure: Spark, Kafka, Ray, Airflow, BigQuery, Redis
Team & Environment
You will be hired across our Consumer and Ads organizations. Potential teams include 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, and Notifications Relevance.
Benefits & Compensation
- Compensation: $185,800 — $303,400 USD + equity in the form of restricted stock units.
- 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
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.
