[Hiring] Senior Machine Learning Engineer II @Instacart

Role Description

As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering.

• Tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data.
• Improve model calibration across surfaces and domains.
• Advance multi-task learning and sequence modeling capabilities.
• Shape next-generation foundation model approach for ads ranking.
• Contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID, and domain language models.

The Ads Response Prediction team owns all systems, algorithms, and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart.

• Includes search and exploration retrieval systems.
• Sequential modeling and generative retrieval systems for next interaction recommendations.
• LLM integrations, relevance models, pCTR models, bidding models, and incrementality models.
• Optimizes for an efficient marketplace to ensure delightful customer shopping experience and desirable advertiser business outcomes.

The team has strong ML infrastructure and MLOps support, including:

• Delta/DBT-Spark data pipelines.
• Ray-based distributed training.
• Automated model deployment.

Qualifications

• PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.
• 6+ 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 familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations.
• Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation.
• Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX).
• Fluency in data manipulation tools (SQL, Spark, 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.

Requirements

• Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics).
• Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures.
• Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges.
• Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning).
• Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar).
• Experience mentoring junior engineers or shaping technical direction for a modeling team.
• Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML).

Benefits

• Highly market-competitive compensation and benefits.
• Remote work flexibility.
• New hire equity grant and annual refresh grants.