Machine Learning Engineer - Recommendations - Shopify

About the role

Great recommendations connect people with products they'll genuinely love. As a Machine Learning Engineer on the Recommendations team, you'll build the personalization technology that powers discovery across Shop surfaces and adjacent recommendation experiences. You'll develop advanced machine learning systems spanning retrieval, ranking, and LLM-based approaches that help millions of buyers discover the right products at the right moment, while supporting millions of merchants in reaching audiences who truly want what they offer.This is an end to end applied ML role: you'll work across data, training, offline evaluation, online experimentation, and streaming/serving infrastructure, with multiple active model families (collaborative filtering / two-tower, LLM-based candidate generation, HSTU /generative approaches, multi-objective rankers) being flighted and iterated in production.Key Responsibilities:Develop and optimize advanced machine learning models for recommendation systems, including retrieval and multi-objective rankingBuild and maintain end-to-end ML pipelines, from feature engineering and training through offline evaluation, online experimentation, and production servingScale LLM-based recommendation systems for production, including throughput optimization, QPS management, multi-partition workers, and operational reliabilityCollaborate closely with platform, infrastructure, and product teams to integrate ML solutions into our unified recommendations pipelineResearch and implement innovative algorithms—including semantic/sequence models, distillation approaches, and relevance grading—to enhance recommendation qualityDocument technical insights, share best practices, and contribute to a strong experimentation culture across engineering teamsQualifications:Extensive experience building and deploying machine learning models for large-scale recommendation, personalization, or ranking systemsStrong proficiency in ML frameworks including TensorFlow or PyTorch, with expert-level Python programming skillsHands-on experience with learning-to-rank approaches and gradient-boosted tree frameworks (XGBoost, CatBoost, LightGBM)Proven analytical skills for processing and extracting insights from large-scale datasets; comfort with BigQuery/dbt, feature stores, and streaming event pipelinesExperience with statistical methods and performance optimization for ML systems at scaleExposure to MLOps and control planes: orchestration, infrastructure-as-code and runtime hardeningExperience with LLM systems in production (prompt/parameter optimization, throughput scaling), semantic embeddings, two-tower architectures, or streaming serving (e.g., Flink)Ready to help shape how millions of buyers discover what they'll love next? Join the team building the recommendation systems that make Shop feel personal at scale.At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.This role may require on-call workorkGreat recommendations connect people with products they'll genuinely love. As a Machine Learning Engineer on the Recommendations team, you'll build the personalization technology that powers discovery across Shop surfaces and adjacent recommendation experiences. You'll develop advanced machine learning systems spanning retrieval, ranking, and LLM-based approaches that help millions of buyers discover the right products at the right moment, while supporting millions of merchants in reaching audiences who truly want what they offer.This is an end to end applied ML role: you'll work across data, training, offline evaluation, online experimentation, and streaming/serving infrastructure, with multiple active model families (collaborative filtering / two-tower, LLM-based candidate generation, HSTU /generative approaches, multi-objective rankers) being flighted and iterated in production.Key Responsibilities:Develop and optimize advanced machine learning models for recommendation systems, including retrieval and multi-objective rankingBuild and maintain end-to-end ML pipelines, from feature engineering and training through offline evaluation, online experimentation, and production servingScale LLM-based recommendation systems for production, including throughput optimization, QPS management, multi-partition workers, and operational reliabilityCollaborate closely with platform, infrastructure, and product teams to integrate ML solutions into our unified recommendations pipelineResearch and implement innovative algorithms—including semantic/sequence models, distillation approaches, and relevance grading—to enhance recommendation qualityDocument technical insights, share best practices, and contribute to a strong experimentation culture across engineering teamsQualifications:Extensive experience building and deploying machine learning models for large-scale recommendation, personalization, or ranking systemsStrong proficiency in ML frameworks including TensorFlow or PyTorch, with expert-level Python programming skillsHands-on experience with learning-to-rank approaches and gradient-boosted tree frameworks (XGBoost, CatBoost, LightGBM)Proven analytical skills for processing and extracting insights from large-scale datasets; comfort with BigQuery/dbt, feature stores, and streaming event pipelinesExperience with statistical methods and performance optimization for ML systems at scaleExposure to MLOps and control planes: orchestration, infrastructure-as-code and runtime hardeningExperience with LLM systems in production (prompt/parameter optimization, throughput scaling), semantic embeddings, two-tower architectures, or streaming serving (e.g., Flink)Ready to help shape how millions of buyers discover what they'll love next? Join the team building the recommendation systems that make Shop feel personal at scale.At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.This role may require on-call workork

Great recommendations connect people with products they'll genuinely love. As a Machine Learning Engineer on the Recommendations team, you'll build the personalization technology that powers discovery across Shop surfaces and adjacent recommendation experiences. You'll develop advanced machine learning systems spanning retrieval, ranking, and LLM-based approaches that help millions of buyers discover the right products at the right moment, while supporting millions of merchants in reaching audiences who truly want what they offer.

This is an end to end applied ML role: you'll work across data, training, offline evaluation, online experimentation, and streaming/serving infrastructure, with multiple active model families (collaborative filtering / two-tower, LLM-based candidate generation, HSTU /generative approaches, multi-objective rankers) being flighted and iterated in production.

Key Responsibilities:

Key Responsibilities:

• Develop and optimize advanced machine learning models for recommendation systems, including retrieval and multi-objective ranking

Develop and optimize advanced machine learning models for recommendation systems, including retrieval and multi-objective ranking

• Build and maintain end-to-end ML pipelines, from feature engineering and training through offline evaluation, online experimentation, and production serving

Build and maintain end-to-end ML pipelines, from feature engineering and training through offline evaluation, online experimentation, and production serving

• Scale LLM-based recommendation systems for production, including throughput optimization, QPS management, multi-partition workers, and operational reliability

Scale LLM-based recommendation systems for production, including throughput optimization, QPS management, multi-partition workers, and operational reliability

• Collaborate closely with platform, infrastructure, and product teams to integrate ML solutions into our unified recommendations pipeline

Collaborate closely with platform, infrastructure, and product teams to integrate ML solutions into our unified recommendations pipeline

• Research and implement innovative algorithms—including semantic/sequence models, distillation approaches, and relevance grading—to enhance recommendation quality

Research and implement innovative algorithms—including semantic/sequence models, distillation approaches, and relevance grading—to enhance recommendation quality

• Document technical insights, share best practices, and contribute to a strong experimentation culture across engineering teams

Document technical insights, share best practices, and contribute to a strong experimentation culture across engineering teams

Qualifications:

Qualifications:

• Extensive experience building and deploying machine learning models for large-scale recommendation, personalization, or ranking systems

Extensive experience building and deploying machine learning models for large-scale recommendation, personalization, or ranking systems

• Strong proficiency in ML frameworks including TensorFlow or PyTorch, with expert-level Python programming skills

Strong proficiency in ML frameworks including TensorFlow or PyTorch, with expert-level Python programming skills

• Hands-on experience with learning-to-rank approaches and gradient-boosted tree frameworks (XGBoost, CatBoost, LightGBM)

Hands-on experience with learning-to-rank approaches and gradient-boosted tree frameworks (XGBoost, CatBoost, LightGBM)

• Proven analytical skills for processing and extracting insights from large-scale datasets; comfort with BigQuery/dbt, feature stores, and streaming event pipelines

Proven analytical skills for processing and extracting insights from large-scale datasets; comfort with BigQuery/dbt, feature stores, and streaming event pipelines

• Experience with statistical methods and performance optimization for ML systems at scale

Experience with statistical methods and performance optimization for ML systems at scale

• Exposure to MLOps and control planes: orchestration, infrastructure-as-code and runtime hardening

Exposure to MLOps and control planes: orchestration, infrastructure-as-code and runtime hardening

• Experience with LLM systems in production (prompt/parameter optimization, throughput scaling), semantic embeddings, two-tower architectures, or streaming serving (e.g., Flink)

Experience with LLM systems in production (prompt/parameter optimization, throughput scaling), semantic embeddings, two-tower architectures, or streaming serving (e.g., Flink)

Ready to help shape how millions of buyers discover what they'll love next? Join the team building the recommendation systems that make Shop feel personal at scale.At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.This role may require on-call workork

Ready to help shape how millions of buyers discover what they'll love next? Join the team building the recommendation systems that make Shop feel personal at scale.