Senior Machine Learning Engineer, Ads Response Prediction

Instacart

. Own and execute research and development of pCTR and conversion prediction models .

Posted 9/15/2026full-timeRemote • CanadaSenior💰 CA$180,000 - CA$190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and implementing pCTR and conversion prediction models, with a strong foundation in causal inference and bias mitigation techniques. Proficient in Python and deep learning frameworks, capable of translating complex modeling problems into actionable ML research directions.

Highest-signal resume keywords

Machine Learning ResearchCTR/Conversion Prediction ModelingDeep Learning FrameworksCausal InferenceData Bias Mitigation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel CalibrationDebiasing TechniquesMulti-Task LearningSequence ModelingStatistical AnalysisModel EvaluationData Analysis

Soft Skills

Communication SkillsCollaborationProblem-Solving

Tools & Technologies

PythonPyTorchTensorFlowJAXSQLSparkPandas

Certifications & Qualifications

Master's DegreePhD

Industry Keywords

CTR ModelingConversion PredictionRecommendation SystemsRanking ProblemsMachine Learning Techniques

Tech Stack

Tools & technologies

PandasPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Own and execute research and development of pCTR and conversion prediction models
Improve calibration, reduce training data biases, and advance model accuracy across Instacart ads surfaces
Design and implement debiasing techniques including Mixed Negative Sampling, Inverse Propensity Weighting, counterfactual risk minimization, Platt scaling, and isotonic regression
Contribute to Multi-Domain Multi-Task model architecture using Mixture-of-Experts, Transformer layers, and LoRA adapters
Contribute to sequence modeling initiatives including TIGER generative retrieval and Semantic ID representation learning
Collaborate on Foundation Models using autoregressive user behavior prediction
Formulate ambiguous modeling problems and translate business observations into ML research directions with clear evaluation criteria
Publish and present findings internally
Contribute through design reviews, paper sharing, and experiment retrospectives

Requirements

What you’ll need

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, including PhD research, applying ML to ranking, recommendation, or prediction problems at scale
Deep understanding of CTR/conversion prediction modeling, including Deep & Wide, DeepFM, DCN, and multi-task learning
Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation
Ability to reason about selection bias, position bias, and propensity-based correction methods
Proficiency in Python and deep learning frameworks: PyTorch, TensorFlow, and JAX
Fluency in SQL, Spark, and Pandas
Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation
Strong written and verbal communication skills
Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists

Benefits

Comp & perks

Flexible choice of work location: home, office, or favorite coffee shop
Regular in-person events
New hire equity grant
Annual refresh equity grants
Market-competitive compensation and benefits