Machine Learning Engineer

Reddit is seeking a Machine Learning Engineer to build and operate production machine learning systems across its platform. The role owns the ML lifecycle, develops scalable pipelines, improves system performance, and partners with cross-functional teams to deliver ML solutions.

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 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

Preferred

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

Recommender systems

ranking systems

Advertising

auction systems

Large-scale representation learning

Multimodal embedding systems

Distributed systems

Large-scale data processing

Apache Spark

Apache Kafka

Ray

Apache Airflow

BigQuery

Redis

Real-time systems

Low-latency production environments

Feature engineering

Model optimization

Production monitoring

LLM

generative AI

LLM evaluation

AI alignment

Fine-tuning

Knowledge distillation

Retrieval-augmented generation

Agentic systems

LLM product productionization

Advanced degree

Computer Science

Machine Learning

Quantitative fields

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

Equity in the form of restricted stock units

Depending on the position offered, commission

Medical, dental, and vision insurance

Generous time off for vacation

Company

Online community platform for topic-based discussions

San Francisco, California, USA

https://www.redditinc.com/

Founded in 2005

1001-5000 employees

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

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

Required skills

Machine Learning, Python, Java, Go, Statistical Learning, XGBoost, Random Forests, Regression, Deep Learning, Transformers, CNNs, GNNs, PyTorch, TensorFlow, ML Pipelines, Data Processing, Model Serving, Recommender Systems, Search and Ranking Systems, Advertising Systems, Distributed Systems, Spark, Kafka, Ray, Airflow, BigQuery, Redis, Real-Time Systems, Feature Engineering, Model Optimization, LLM Evaluation and Alignment, Fine-Tuning, Knowledge Distillation, RAG

Experience

3+ years