Machine Learning Engineer

7h•$185,800 - $260,100

About The Position

Reddit is hiring Machine Learning Engineers across its Consumer and Ads organizations to build end-to-end machine learning systems, from research and modeling through production deployment. The role focuses on large-scale applied ML that supports discovery, relevance, advertising, and monetization across Reddit’s platform. Engineers may work on personalized recommendations, search and ranking, advertising systems, content and user understanding, ML pipelines and model-serving infrastructure, real-time decision systems, and applied AI or LLM-driven experiences. The position operates at internet scale and can directly influence user experience, advertiser value, and business outcomes. The role involves owning the full machine learning lifecycle, including problem definition, feature engineering, training, evaluation, deployment, and monitoring. Machine Learning Engineers build reliable and observable pipelines, work with large datasets, improve system performance and measurable outcomes, collaborate with cross-functional partners, and contribute to technical strategy, architecture, and the long-term ML roadmap. The posting may span more than one career level and identifies potential teams across advertising, feed relevance, search, notifications, and ML understanding.

Requirements

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.
Strong grasp of machine learning algorithms, from classic statistical learning such as XGBoost, Random Forests, and regression to deep learning architectures such as Transformers, CNNs, and GNNs.
Hands-on experience with modern machine learning frameworks such as PyTorch or 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 Haves

Experience with recommender systems, search or ranking systems, advertising or auction systems, large-scale representation learning, or multimodal embedding systems.
Familiarity with distributed systems and large-scale data processing tools such as Spark, Kafka, Ray, Airflow, BigQuery, or Redis.
Experience working with real-time systems and low-latency production environments.
Background in feature engineering, model optimization, and production monitoring.
Experience with LLM or generative AI techniques, including LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG or agentic systems, and productionizing LLM-powered products at scale.
Advanced degree in Computer Science, Machine Learning, or a related quantitative field.

Responsibilities

Design, build, and deploy production-grade machine learning models and systems at scale.
Own the full ML lifecycle, from problem definition and feature engineering through 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 and user understanding, and optimization systems.
Partner 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 and transformer-based methods, and LLM evaluation and alignment.
Contribute to technical strategy, architecture, and the long-term ML roadmap.

Benefits

Comprehensive healthcare benefits and income replacement programs.
401(k) with employer match.
Global benefit programs supporting workspace, professional development, and caregiving.
Family planning support.
Gender-affirming care.
Mental health and coaching benefits.
Flexible vacation and paid volunteer time off.
Generous paid parental leave.
Equity in the form of restricted stock units; some positions may also be eligible for commission.
Medical, dental, and vision insurance for U.S.-based employees.