Senior ML Engineer, Recommendation Systems
Skills
- Proven experience shipping production ML systems for personalization and recommendation
- Deep knowledge of ranking algorithms (collaborative filtering, learning-to-rank, deep learning)
- Strong Python proficiency and experience with TensorFlow or PyTorch
- Advanced SQL skills and experience with modern data warehouses (Snowflake, BigQuery, Redshift)
- Familiarity with distributed computing frameworks (Spark, Ray) and real-time systems
- Experience with A/B testing platforms, experiment design, and logging best practices
- Practical experience optimizing latency, throughput, and cost in production ML systems
Responsibilities
- Design, train, and deploy ranking and recommendation models that serve 100M+ predictions per day
- Build and maintain scalable data pipelines and feature engineering workflows (Spark, Beam, Dask)
- Implement real-time personalization with latency targets (<50ms) and optimize for throughput and cost
- Run statistically rigorous A/B tests and analyze results to measure business impact
- Instrument model monitoring, alerting, and retraining pipelines to ensure reliability
- Collaborate with product, engineering, and analytics teams to scope and launch personalization features
- Balance relevance, diversity, and revenue objectives within ranking algorithms
Education
- Bachelor’s degree in Computer Science, Engineering, Statistics, or equivalent practical experience
- Graduate degree (MS/PhD) in a relevant field is a plus but not required
- Strong foundations in machine learning, statistics, and software engineering
Benefits
- Competitive base salary with profit-sharing bonus
- Remote-first culture with flexible work arrangements
- Career acceleration and high-ownership roles within a high-growth company
- Comprehensive benefits package (healthcare and other standard benefits)
- Opportunity to work on high-scale systems and influence product direction
Average salary estimate
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