Senior Machine Learning Engineer - Search & Recommendations Ranking
Instacart
Senior Machine Learning Engineer - Search & Recommendations Ranking
Job Description
Responsibilities
- Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
- Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
- Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
- Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
- Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
Required Qualifications
- 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
- Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
- Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
- Excellent analytical skills and strong cross-functional communication abilities.
Preferred Qualifications
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
- Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.
Required Skills
- ML at scale
- ranking or recommendation systems
- multi-objective or constrained optimization
- online testing and attribution
- Python
- SQL/Pandas
- XGBoost
- TensorFlow/PyTorch
- analytical skills
- cross-functional communication
- multi-task learning architectures (MMOE/PLE, shared encoders)
- calibration
- counterfactual evaluation
- uplift/causal modeling
- contextual bandits
- low-latency ranking services
- feature stores
- caching
- vector + lexical retrieval
- re-ranking
- A/B testing infrastructure
- LLMs (embeddings, adapter tuning)
Salary Range
$196000 - $263000 USD
Locations
- San Francisco, CA, United States, San Francisco, CA, United States
Salary
196,000 - 263,000 USD / yearly
Skills Required
- ML at scaleintermediate
- ranking or recommendation systemsintermediate
- multi-objective or constrained optimizationintermediate
- online testing and attributionintermediate
- Pythonintermediate
- SQL/Pandasintermediate
- XGBoostintermediate
- TensorFlow/PyTorchintermediate
- analytical skillsintermediate
- cross-functional communicationintermediate
- multi-task learning architectures (MMOE/PLE, shared encoders)intermediate
- calibrationintermediate
- counterfactual evaluationintermediate
- uplift/causal modelingintermediate
- contextual banditsintermediate
- low-latency ranking servicesintermediate
- feature storesintermediate
- cachingintermediate
- vector + lexical retrievalintermediate
- re-rankingintermediate
- A/B testing infrastructureintermediate
- LLMs (embeddings, adapter tuning)intermediate
Required Qualifications
- 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production. (experience)
- Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR. (experience)
- Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch). (experience)
- Excellent analytical skills and strong cross-functional communication abilities. (experience)
Preferred Qualifications
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration. (experience)
- Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference. (experience)
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate. (experience)
Responsibilities
- Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
- Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
- Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
- Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
- Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
Tags & Categories
Senior Machine Learning Engineer - Search & Recommendations Ranking
Instacart
Senior Machine Learning Engineer - Search & Recommendations Ranking
Job Description
Responsibilities
- Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
- Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
- Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
- Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
- Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
Required Qualifications
- 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
- Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
- Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
- Excellent analytical skills and strong cross-functional communication abilities.
Preferred Qualifications
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
- Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.
Required Skills
- ML at scale
- ranking or recommendation systems
- multi-objective or constrained optimization
- online testing and attribution
- Python
- SQL/Pandas
- XGBoost
- TensorFlow/PyTorch
- analytical skills
- cross-functional communication
- multi-task learning architectures (MMOE/PLE, shared encoders)
- calibration
- counterfactual evaluation
- uplift/causal modeling
- contextual bandits
- low-latency ranking services
- feature stores
- caching
- vector + lexical retrieval
- re-ranking
- A/B testing infrastructure
- LLMs (embeddings, adapter tuning)
Salary Range
$196000 - $263000 USD
Locations
- San Francisco, CA, United States, San Francisco, CA, United States
Salary
196,000 - 263,000 USD / yearly
Skills Required
- ML at scaleintermediate
- ranking or recommendation systemsintermediate
- multi-objective or constrained optimizationintermediate
- online testing and attributionintermediate
- Pythonintermediate
- SQL/Pandasintermediate
- XGBoostintermediate
- TensorFlow/PyTorchintermediate
- analytical skillsintermediate
- cross-functional communicationintermediate
- multi-task learning architectures (MMOE/PLE, shared encoders)intermediate
- calibrationintermediate
- counterfactual evaluationintermediate
- uplift/causal modelingintermediate
- contextual banditsintermediate
- low-latency ranking servicesintermediate
- feature storesintermediate
- cachingintermediate
- vector + lexical retrievalintermediate
- re-rankingintermediate
- A/B testing infrastructureintermediate
- LLMs (embeddings, adapter tuning)intermediate
Required Qualifications
- 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production. (experience)
- Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR. (experience)
- Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch). (experience)
- Excellent analytical skills and strong cross-functional communication abilities. (experience)
Preferred Qualifications
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration. (experience)
- Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference. (experience)
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate. (experience)
Responsibilities
- Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
- Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
- Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
- Partner across ads, infrastructure, product, and design teams to translate business goals into ranking policies and measurable ROI.
- Mentor ML engineers to build expertise in ranking, causal inference, and scalable serving systems.
Tags & Categories