Senior Machine Learning Engineer II, Ads Response Prediction

Instacart

Confirmed live 2 days ago High trust
Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$240,000–$253,500 / yr
Posted
106 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $228k
This role $247k
$172k most similar roles pay here $300k

This role pays more than 59% of similar roles. Most pay $197,925–$257,515 — the shaded band above. At the midpoint, this role pays about $247k versus about $228k for comparable roles.

Based on 240 similar postings.

Employer

About Instacart

Instacart (Maplebear Inc.) is a North American grocery technology company that operates an online marketplace and delivery service connecting customers with personal shoppers who pick and deliver orders from local retailers. It also sells retail e-commerce software and advertising services to grocers and brands.

Instacart currently has 49 open roles on FindRole.

Listed pay typically runs $196,000–$207,000 across 49 roles with salary data.

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View all roles at Instacart

At a glance

TL;DR · Senior Machine Learning Engineer II, Ads Response Prediction

As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the research and development of core machine learning models powering the ads ecosystem. This research-leaning role focuses on theoretical problem formulation, training methodology, and model quality rather than infrastructure. You will build pCTR and conversion prediction models while addressing challenges like selection bias, position bias, and optimizer’s curse. Your work involves implementing debiasing techniques, multi-task learning, and sequence modeling using architectures like Mixture-of-Experts and Transformer layers. You will also contribute to generative retrieval systems and foundation model approaches for ads ranking. Required skills include proficiency in Python, PyTorch, TensorFlow, or JAX, along with SQL and Spark. The role addresses the technical challenge of creating a relevant, engaging advertising experience by optimizing marketplace dynamics and improving model calibration across various surfaces.

What does a Machine Learning Engineer earn in Remote?

Median $229700 from 72 postings across 27 companies.

See salary data

What you'll do

  • Lead the research and development of pCTR and conversion prediction models to improve accuracy across ad surfaces.
  • Implement debiasing techniques like Mixed Negative Sampling and Inverse Propensity Weighting to mitigate selection and position bias.
  • Develop Multi-Domain Multi-Task model architectures using Mixture-of-Experts, Transformer layers, and LoRA adaptors.
  • Drive sequence modeling initiatives including TIGER generative retrieval systems and Semantic ID representation learning.
  • Translate ambiguous business problems into well-defined machine learning research directions with clear evaluation criteria.
  • Contribute to the development of foundation models for ads ranking using autoregressive user behavior prediction.
  • Present technical findings internally through design reviews, paper sharing, and experiment retrospectives.

What we're looking for

  • Master's or PhD degree in machine learning, statistics, computer science, information retrieval, or a related quantitative field.
  • 6+ 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 like Deep & Wide, DeepFM, DCN, and multi-task learning.
  • Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation techniques.
  • Proficiency in Python, deep learning frameworks (PyTorch, TensorFlow, JAX), and data tools (SQL, Spark, Pandas).
  • Proven ability to translate ambiguous business problems into well-defined ML research directions with clear evaluation criteria.
  • Strong written and verbal communication skills to explain complex modeling decisions to cross-functional stakeholders.

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