JobHabor

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
Apply on the employer’s site

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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