Senior Machine Learning Engineer, Search & Recommendations
Instacart
- Location
- Ontario · Alberta · British Columbia +1
- Workplace
- Remote
- Employment
- Full Time
- Salary
- CAD 180,000–190,000/yr
Posted 3mo ago
The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.
Responsibilities
- Architect the ranking backbone unifying query understanding, personalization, multi-objective ranking, ads, and merchandising
- Design and build a search autosuggest system optimized for personalization and value-based relevance
- Design long-horizon objective functions and build uplift/causal value models
- Develop production-grade Multi-Task Learning models
- Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and latency optimization
- Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines
- Partner across 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
Requirements
- 4+ years applying ML at scale with a Master’s degree, or 2+ years for PhD
- Proven track record improving ranking or recommendation systems in production
- Demonstrated success in applying multi-objective or constrained optimization
- Experience with online testing and attribution beyond CTR
- Strong coding (Python)
- Strong data fluency (SQL/Pandas)
- Expertise in classic ML techniques (e.g., XGBoost)
- Expertise in deep learning frameworks (TensorFlow/PyTorch)
- Excellent analytical skills
- Strong cross-functional communication abilities
Preferred
- Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders)
- Expertise in calibration, counterfactual evaluation, uplift/causal modeling
- Expertise in contextual bandits for exploration
- Experience building low-latency ranking services
- Experience with feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure
- Expertise in constraint-aware inference
- Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning)
Skills
- XGBoost
- TensorFlow
- PyTorch
- Python
- SQL
- Pandas
- LLM
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