Senior Machine Learning Engineer, Ads Response Prediction

Instacart
Ontario, Alberta, British Columbia, Nova Scotia
Workplace: RemoteFull timeCAD 180,000 - 190,000 annuallyFunction: Data Science & Machine LearningExperience: 3+ yearsEducation: mastersSkills: ["Communication","Problem-solving","Experiment design","Collaboration","Technical rigor"]

Own and develop pCTR and conversion prediction models powering Instacart Ads. Tackle research-heavy challenges like selection/position bias and optimizer’s curse, improve calibration, and advance multi-domain multi-task and sequence modeling. Work on next-generation architectures (MDMT, MoE, transformers) and generative retrieval systems such as TIGER and Semantic ID representations, collaborating with ML and MLOps teams to deliver measurable gains across ad surfaces and marketplace outcomes.

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FursaFursa
Instacart
Instacart
19 hours ago

Senior Machine Learning Engineer, Ads Response Prediction

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Source: Company careers pageValidated by: Fursa AI
Last checked: 48 minutes agoStatus: Live

Job Summary

Own and develop pCTR and conversion prediction models powering Instacart Ads. Tackle research-heavy challenges like selection/position bias and optimizer’s curse, improve calibration, and advance multi-domain multi-task and sequence modeling. Work on next-generation architectures (MDMT, MoE, transformers) and generative retrieval systems such as TIGER and Semantic ID representations, collaborating with ML and MLOps teams to deliver measurable gains across ad surfaces and marketplace outcomes.
Location: Ontario, Alberta, British Columbia, Nova Scotia
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Own and execute R&D for pCTR and conversion prediction models, improving calibration, reducing training data biases, and increasing accuracy across ads surfaces.
  • •Design and implement debiasing and calibration techniques including Mixed Negative Sampling, Inverse Propensity Weighting, counterfactual risk minimization, Platt scaling, and isotonic regression.
  • •Advance multi-domain multi-task and sequence modeling via architectures such as MDMT, Mixture-of-Experts, transformer layers, and LoRA adapters for scalable domain fine-tuning.
  • •Contribute to TIGER generative retrieval system and Semantic ID representation learning, expanding their use across ads placements.
  • •Formulate research problem scope from first principles, evaluate rigorously, and publish/present findings with experiment retrospectives and paper sharing.

Pay and Benefits

Salary: CAD 180,000 - 190,000 annually
Equity and Bonus:Equity

Key Requirements

  • •Master’s or PhD in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field, or equivalent experience.
  • •3+ years of combined academic and industry experience applying ML to ranking, recommendation, or prediction problems at scale.
  • •Deep understanding of CTR/conversion prediction modeling, including architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning.
  • •Strong causal inference and counterfactual reasoning with ability to mitigate training data biases (selection bias, position bias) using propensity-based correction methods.
  • •Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX), plus data tools (SQL, Spark, Pandas).
Experience:3+ yearsAdsRankingRecommendationPrediction
Education:Master's
Skills:CommunicationProblem-solvingExperiment designCollaborationTechnical rigor
Languages:English
Tech Stack:PythonPyTorchTensorflowJAXSQLSparkPandasDeltaDBT-SparkRayDeep & WideDeepFMDCNTransformerMixture-of-ExpertsMoELoRAPlatt scalingIsotonic regressionInverse Propensity Weighting

Company Brief

Instacart
Operates an online grocery marketplace connecting customers with personal shoppers and local retailers for grocery delivery and pickup. Provides technology, logistics, and advertising solutions to supermarkets and consumer packaged goods brands.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2012
Glassdoor
Glassdoor: 3.6
WebsiteLinkedIn