Recommendation Systems Engineer Resume Example

Professional Summary

Recommendation systems engineer with 5+ years of experience building personalization platforms for e-commerce, streaming, and content applications. Deep expertise in collaborative filtering, deep learning recommenders, and real-time feature serving at scale with measurable impact on engagement and revenue.

Key Skills

What to Include on a Recommendation Systems Engineer Resume

• A concise summary that states your recommendation systems engineer experience level, strongest domain, and the business problems you solve.
• A skills section that mirrors the job description language for Python, PyTorch, TensorFlow Recommenders, Spark.
• Experience bullets that connect recommendation systems, collaborative filtering, content-based filtering to measurable outcomes such as cost savings, faster delivery, better quality, or improved customer results.
• Tools, platforms, certifications, and methods that are current for ai & machine learning roles.
• Recent projects that show ownership, cross-functional work, and a clear result instead of generic responsibilities.

Sample Experience Bullets

• Built a two-stage recommendation system (candidate generation + ranking) serving 15M daily active users, increasing click-through rate by 23% and driving $12M in incremental annual revenue compared to the previous popularity-based approach.
• Designed a real-time feature serving pipeline using Feast and Redis that computes and serves 200+ user and item features with p99 latency under 10ms, supporting 50K recommendation requests per second at peak traffic.
• Implemented a neural collaborative filtering model in PyTorch that improved offline NDCG@10 by 18% over the matrix factorization baseline, validated through a 4-week A/B test showing a 9.5% increase in user session duration.
• Solved the cold-start problem for new users by building a content-based fallback model using item metadata embeddings, achieving 72% of the engagement rate of the fully personalized model within the first 3 user interactions.
• Developed a multi-objective ranking model that balanced relevance, diversity, and business objectives (margin, inventory clearance), increasing catalog coverage from 15% to 45% while maintaining click-through rates within 2% of the relevance-only model.
• Built an approximate nearest neighbor index using FAISS over 5M item embeddings that retrieves 100 candidates in under 2ms, replacing a brute-force search that took 500ms and was the primary bottleneck in the recommendation pipeline.
• Created an offline evaluation framework that tested recommendation models against 6 metrics (precision, recall, NDCG, coverage, novelty, diversity) across 10 user segments, reducing the number of unnecessary online A/B tests by 40%.
• Implemented a Kafka-based event streaming pipeline that captures user interactions in real-time and updates user profiles within 30 seconds, enabling session-aware recommendations that improved next-click prediction accuracy by 15%.
• Designed and ran 15+ A/B tests for recommendation algorithm changes, each with 500K+ users per variant, using sequential testing methodology that allowed early stopping and reduced average test duration from 4 weeks to 2.5 weeks.
• Reduced model training costs by 55% by implementing incremental training that updates the recommendation model daily on new interaction data rather than retraining from scratch, while maintaining model quality within 1% of full retraining.
• Partnered with the trust and safety team to implement post-ranking filters that removed policy-violating content from recommendations, processing 100M+ recommendation responses daily with a false positive rate below 0.1%.

ATS Keywords for Recommendation Systems Engineer Resumes

Use these terms naturally where they match your experience and the job description.

Recommended Certifications

• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning - Specialty

What Does a Recommendation Systems Engineer Do?

• Design, develop, and maintain software solutions using Python, PyTorch, TensorFlow Recommenders and related technologies
• Collaborate with cross-functional teams including product managers, designers, and QA engineers to deliver features on schedule
• Write clean, well-tested code following industry best practices for recommendation systems and collaborative filtering
• Participate in code reviews, technical discussions, and architecture decisions to improve system quality and team knowledge
• Troubleshoot production issues, optimize performance, and ensure system reliability across all environments

Resume Tips for Recommendation Systems Engineers