Senior ML Engineer: Real-Time Personalization & Ads

Aimlroles; Northern (KY); Hybrid; USD 217,000 - 303,000; Full time

Benefits offered by this job

Healthcare benefits

401k match

Global benefits

Family planning

Gender-affirming care

Mental health support

Flexible vacation

Parental leave

Job summary

Reddit is seeking a Machine Learning Engineer to design, build, and deploy production ML systems at internet scale across consumer and ads teams. You will own full ML lifecycles—from problem framing and feature engineering to training, evaluation, deployment, and monitoring—while improving ranking, recommendations, and relevance.

You’ll partner with product and data science to translate complex problems into robust ML solutions.

Qualifications

• 3–5+ years of experience building, deploying, and operating ML systems in production.
• Strong programming skills in Python, Java, Go, or similar languages with solid software engineering fundamentals.
• Experience with modern ML frameworks (PyTorch, TensorFlow) and scalable ML pipelines.

Responsibilities

• Design, build, and deploy production-grade ML models and systems at scale.
• Own the full ML lifecycle from problem definition to deployment and monitoring.
• Build scalable data and model pipelines with reliability and observability.
• Work with large-scale datasets to improve ranking, recommendations, search relevance, and signals.
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams.
• Improve system performance across latency, throughput, and quality metrics.
• Research and apply state-of-the-art ML/AI techniques and contribute to ML roadmap.

Skills

Python/Java/Go

ML fundamentals

PyTorch/TensorFlow

Production ML

Cross-functional

Model deployment

Education

Advanced degree in Computer Science or related field

Tools

Spark

Kafka

Ray

Airflow

BigQuery

Redis

Job description

Reddit is seeking a Machine Learning Engineer to design, build, and deploy production ML systems at internet scale across consumer and ads teams. You will own full ML lifecycles—from problem framing and feature engineering to training, evaluation, deployment, and monitoring—while improving ranking, recommendations, and relevance.

You’ll partner with product and data science to translate complex problems into robust ML solutions.