Overview

Instacart powers millions of shopping journeys every day across one of the largest online grocery catalogs in the world. Machine learning sits at the center of that experience, shaping how customers search, discover, and decide what to buy.

Advances in generative AI create a rare opportunity to rethink how commerce works. Instead of traditional ranking pipelines alone, the next generation of shopping experiences will be powered by intelligent systems that help customers explore products, plan purchases, and discover what they need more naturally.

We are looking for a Senior Staff Machine Learning Engineer to help lead this transformation.

In this role, you will define the technical vision for how machine learning powers the Instacart shopping journey and build systems that move beyond traditional search and recommendation approaches. You will help develop next generation shopping experiences using large language models, agentic AI systems, and large scale personalization.

This is a hands-on IC and technical leadership role at the Director level with no people management responsibilities. You will shape long term technical direction while building production systems that serve millions of customers in real time.

About the Job

• Define and drive the long term technical vision for Shopping ML across search, discovery, recommendations, and personalization.
• Lead development of AI systems that fundamentally improve how customers discover products and shop on Instacart.
• Architect large scale ML systems combining LLM reasoning, retrieval, ranking, recommendation models, and personalization.
• Build and operate production ML systems powering real time shopping experiences across millions of users and products.
• Identify foundational ML capabilities that unlock new customer experiences and accelerate experimentation across the organization.
• Partner with senior leaders across product, engineering, and data science to shape technical strategy and roadmap.
• Influence company wide ML architecture and contribute to major technical initiatives across Instacart.
• Mentor senior/staff engineers through design reviews, architecture guidance, and hands-on collaboration.
  
  

About You

Minimum Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field, or equivalent industry experience.
• 10+ years building and operating large scale machine learning systems in production.
• Proven track record architecting ML systems that power core product experiences at global scale.
• Deep expertise in areas such as recommendation systems, ranking models, embeddings, representation learning, or search systems.
• Experience combining generative AI with traditional ML systems such as retrieval, ranking, and personalization.
• Experience designing ML platforms that support large scale experimentation, training, and deployment.
• Ability to influence senior engineering and product leaders and drive technical strategy.
• Experience mentoring engineers and raising the technical bar across teams.

Preferred Qualifications

• Experience building search, discovery, or recommendation systems for large consumer platforms.
• Expertise in large language models, agentic AI systems, or tool using AI architectures.
• Experience fine tuning and distilling LLMs for low latency production systems, balancing model quality, latency, and cost.
• Experience building ML infrastructure supporting large scale experimentation and model deployment.
• Background working on e-commerce, marketplaces, or large consumer platforms.
• Recognized technical leadership through publications, patents, conference talks, or open source contributions.

Overview

About the Role

Are you excited about applying cutting-edge AI / ML models and technologies at the forefront of digital advertising?  Are you inspired to deliver tangible, quantifiable impact to tens of millions of users and partners, while solving some of the toughest optimization challenges in a 4-sided marketplace?  Are you passionate about mentoring and growing ML engineering talent, while instilling and fostering a principled engineering culture?  If so, we have a job for you!  The Ads Engineering team is looking for a seasoned ML engineering leader to act as the Area Tech Lead of Ads Quality.  In this role, you will directly contribute to the company’s myriad of AI, ML, and Quality innovations, both as an Individual Contributor and Technical Lead.  You will lead the team to design next-generation, AI-powered solutions to support the business’ broadening growth.  You will partner with Eng and PM leadership to prioritize and support all product needs.  You will develop company-defining collaborations with other engineering pillars across Instacart.  You will grow and mentor engineers, and contribute to shaping engineering culture not only for Ads but for all of Instacart.

The Ads Engineering team builds E2E, full-stack systems to power Instacart’s industry-leading ads innovations in digital ads’ top growing channel - Retail Media.  As a fast-growing, critical piece of Instacart’s overall business, the Ads Engineering team strives to both meet and support our advertiser partners for their needs today, while also challenging the status quo to push forward next-generation technologies.  The team consists of a broad spectrum of engineers across all levels, with expertise ranging from backend infrastructure to data engineering to machine learning & AI to frontend technologies and more.

About the Job 

• Develop & design innovative AI-powered systems addressing a wide range of Ads Quality challenges, such as: Optimize ads pacing/bidding, retrieval and auction processes toward a diverse set of advertiser goals, balancing short- and long-term objectives,
• Balance between user, advertiser, and retailer needs, and turn such solutions into quantifiable, optimizable formulations,
• Identify and build gen AI solutions to revolutionize all aspects of the quality stack,
• Build advanced generative AI and recommendation frameworks revolutionizing advertiser experience and campaign efficacy,
• Construct systems and frameworks to provide intelligent recommendations and assistance to advertisers,
• Collaborate with Data Scientists to establish metrics and methodologies for both tracking, optimization, and goaling purposes.
• Define & own the Ads Quality technical strategy and roadmap, balancing between both short-term needs and long-term sustainability.
• Demonstrates ability to architect and design complex AI / quality components, considering scalability, reliability, performance, and security.
• Serves as a technical leader and mentor, guiding and empowering junior ML and software engineers through code and design reviews, knowledge sharing, collaborative problem-solving, improving key engineering processes, and fostering a thriving engineering culture.
• Serve as a technical mentor, empowering engineers at all career levels through effective coaching, collaborative code reviews and knowledge sharing to develop the next set of leaders
• Builds and maintains synergies with other ML engineering teams across Instacart; acts as a bridge representing Ads as an influential voice in company-wide technical forums.
• Contributes to company-level technical initiatives as a thought leader and key driver of Instacart’s engineering vision & goals
• Note this is a hands-on IC / TL role at the Director+ level.  There are no people management expectations.

About You

Minimum Qualifications

• PhD in AI / ML or related field, or equivalent academic and research experience.
• 10+ years of experience as a software and / or ML engineer.
• Proven track record of architecting & building production-grade ML systems, AI and quantitative optimization technologies at a global scale.
• Demonstrated ability to lead technical discussions and collaborate effectively at senior to executive levels, driving consensus and clarity.
• Experience mentoring and developing engineering culture and talent.

Preferred Qualifications

• Hands-on expertise in building large-scale AI/ML systems bridging generative AI, predictive modeling, and recommendation engines to solve real-world problems.
• Deep understanding of Ads Quality systems, including pacing/bidding, yield optimization, retrieval processes, and auction dynamics.
• Familiar with AWS, Golang, Spark, Clickhouse, Snowflake, TensorFlow.

OVERVIEW

Since 2012, Instacart has been focused on making grocery delivery convenient, affordable, and accessible to everyone. We bring fresh groceries and everyday essentials to customers across the US and Canada from nearly 55,000 stores across 5,500 markets. Our mission is to create a world where everyone has access to the food they love, and to achieve that goal, we innovate in a wide range of areas including e-commerce, advertising, and fulfillment.

Machine learning is central to how we build intelligent shopping experiences at Instacart. We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. A few examples:

• We build state-of-the-art models powering Search, Discovery, and Ads, combining generative AI and traditional machine learning to create best-in-class recommendations
• We build rich product and knowledge graphs from catalog data imported from hundreds of retailers, applying them in recommendations and other user experiences
• We redefine traditional domains across the company with AI, such as hyperpersonalized marketing and 0 → 1 meal planning products

We are looking for talented Ph.D. students to join our fast-moving ML teams and work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.

ABOUT THE JOB

Based on your passion and background, you may choose to work in a few different areas:

• Query understanding: Using cutting-edge AI and LLM-based techniques to understand user intent, refine queries, and support downstream retrieval and ranking.
• Search relevance and ranking: Improving search relevance by incorporating signals from user behavior, catalog knowledge, and generative models, including hybrid retrieval and ranking systems.
• Generative recommendations: Pushing the boundaries of where generative and traditional models intersect across retrieval and ranking systems; developing scalable feedback and reward modeling approaches for closed-loop learning (RFT).
• LLM evaluation and AIQA systems: Building LLM-based evaluation frameworks (e.g., LLM-as-a-Judge, self-critique) to improve the quality and reliability of generative and agentic systems.
• Low-latency and scalable LLM systems: Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments, balancing quality, cost, and latency through cascading, distillation, and selective generation.
• Knowledge graphs: Working on graph data management and knowledge discovery over one of the world’s largest grocery catalogs, and integrating structured knowledge with LLM-based reasoning and natural language interfaces.
• Sequence modeling: Building temporal models for user behavior prediction.

ABOUT YOU

Minimum Qualifications:

• Ph.D. student in computer science, mathematics, statistics, economics, or related areas.
• Strong programming (Python, Golang) and algorithmic skills.
• Solid foundations in machine learning, algorithms, or optimization
• Curious, self-motivated, and comfortable working on open-ended problems

Preferred Qualifications: 

• Ph.D. student at a top tier university in the United States 
• Hands-on experience with generative or traditional modeling frameworks (PyTorch, Tensorflow, vLLM)
• Prior industry or research internship in machine learning or AI
• Interest and experience in translating research ideas into scalable production systems

Overview

We are looking for interns to join Instacart’s Economics team. The ideal candidate for this role will bring a combination of experience in both economics and machine learning. We are in particular looking for current or recently graduated PhD students in economics or related fields like marketing, finance, or operations research. Candidates should bring some relevant research experience, typically in computationally intensive empirical topics, as well as some exposure to machine learning coursework and applications.

The Economics team at Instacart works on a range of interesting and challenging problems at the intersection of machine learning and economics, from aligning the incentives in our multi-sided marketplace to analyzing the impact of behavioral nudges on our customers’ and shoppers’ decisions. Some of the core areas of focus for our team include online advertising, uplift and long term value modeling, logistics, marketplace optimization (consumers, shoppers, retailers), inventory intelligence, and general causal inference. You can find more information in our blog post that introduces the team and the type of work we do.

About the Job

• You will help design and build end-to-end machine learning solutions.
• You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects.
• You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains.
• You will develop high impact solutions to support Instacart's ambitious growth plans.
• You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments.
• You will have the freedom to suggest and drive organization-wide initiatives.

About You

Minimum Qualifications

• Current or recently graduated PhD student in economics or a related field with focus on data-intense problems.
• A blend of economic theory, applied econometrics, and business acumen that let you jump into a fast-paced environment and contribute from day one.
• Expertise in causal inference with observational and experimental data.
• Expertise in Python or R and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools.
• Self-motivation and a strong sense of ownership

Overview

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. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and 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. This 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. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.

The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.

About the Job

• Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.
• Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.
• Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.
• Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.
• Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.
• Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.
• Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

About You

Minimum 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. Ability to reason about selection bias, position bias, and propensity-based correction methods.
• 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. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists.

Preferred Qualifications

• 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) applied to recommendation or ranking models.
• 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).

Overview

The Advertiser Optimization team is the decision-making engine of Instacart's $1B+ ads business. We own the systems responsible for Bidding, Pacing, Budgeting, and Targeting: converting stated advertiser goals into real-time auction actions. Our mission is to maximize realized Advertiser Value by deciding when to participate, how much to bid, and how fast to spend, all while balancing User Experience and Platform Revenue.

We are hiring a Senior Applied Scientist II to lead the algorithmic direction of these systems. This is a role for someone who thinks in terms of control theory, constrained optimization, and auction economics, and who can translate those frameworks into production code that makes millions of decisions per day. You will formulate problems from first principles, shape the technical roadmap, and own systems end-to-end from mathematical design through production deployment through impact measurement.

About the Job

• Design and evolve real-time bid optimization systems that translate advertiser goals (target ROAS, budget constraints) into optimal auction bids under uncertainty. Formulate the bidding problem as constrained optimization and build the feedback mechanisms that keep bids aligned with realized outcomes.
• Build intelligent budget pacing algorithms that distribute spend across time and auction opportunities. The core challenge: allocating a finite daily budget across stochastic demand while maximizing total value, subject to advertiser constraints and time-varying conversion dynamics.
• Develop the analytical frameworks that connect bidding, pacing, and budgeting into a coherent optimization objective.
• Shape auction mechanics including reserve pricing, multi-slot allocation, and bid-to-price mapping. Reason about mechanism design tradeoffs between advertiser outcomes, platform revenue, and marketplace efficiency.
• Own the full research-to-production loop: diagnose system behavior from large-scale data, formulate hypotheses, design experiments, ship production code, and measure impact. Write technical strategy documents that set the algorithmic direction for the team.

About You

Minimum Qualifications

• MS or PhD in operations research, applied mathematics, control systems, computational economics, or a related quantitative field.
• 8+ years of experience building and deploying optimization or control systems in production environments (not just research prototypes).
• Strong foundation in at least two of: feedback control theory (PID, MPC), convex and stochastic optimization, auction theory and mechanism design, dynamic programming.
• Proficiency in one of the following languages: Go, Java, C++ for production systems and Python for data analysis and offline pipelines.
• Demonstrated ability to translate mathematical formulations into production code that runs at scale (millions of decisions per day, sub-100ms latency constraints).

Preferred Qualifications

• Experience with real-time bidding systems, ad auction optimization, or computational advertising at scale.
• Background in budget-constrained allocation methods. Experience with adaptive control or model-predictive control in production systems.
• Familiarity with causal inference and experimental design for evaluating algorithmic changes in marketplace settings.
• Track record of shaping technical strategy and driving cross-functional alignment between engineering, product, and data science.

Overview:

We are looking for a Senior Machine Learning Engineer with a strong Operations Research background to join the Service Availability & Routing team within Instacart's Logistics organization. In this role, you will work at the intersection of combinatorial optimization, mathematical programming, and AI to solve high-impact problems in the fulfillment space — including order batching, shopper routing, service availability prediction, and real-time assignment. You'll partner closely with engineering, product, and data science to ship models and algorithms that directly influence Instacart's profitability and shopper experience at scale.

The Logistics & ML group is responsible for the intelligence and execution behind Instacart’s fulfillment system. The team optimizes a multi-sided marketplace to ensure customers get their orders on-time and in high quality, shoppers get efficient and fulfilling work, and retailers and consumer brands get reasonable business. The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial, as well as foundational problems executing on a high throughput system with dynamic data.

About the Job:

• Design, develop, and deploy machine learning solutions to tackle practical challenges in the marketplace.
• Collaborate closely with product managers, data scientists, and backend engineers to deeply understand business needs and create impactful ML applications.
• Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals.
• Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.

About You:

Minimum Qualifications:

• Have a graduate degree (masters or PhD) in Operations Research or Industrial Engineering
• 3+ years of industry experience using machine learning to solve real-world problems with large datasets
• Have strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and Machine Learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
• Have strong analytical skills and problem-solving ability
• Are a strong communicator who can collaborate with diverse stakeholders across all levels

Preferred Qualifications:

• Knowledge of deep learning frameworks and methodologies
• Experience in applying machine learning and optimization techniques to solve marketplace problems

#LI-Remote

Overview

The Search & Personalization ML team is Instacart’s engine for state-of-the-art multi-task, multi-objective ranking—unifying search, discovery, recommendation, ads, and merchandising into a single value-aware platform. Partnering with world-class engineers, scientists, and PMs, we build the ranking backbone that powers every pixel of the shopping journey, optimizing not just for clicks, but for incremental GTV, basket lift, and retention over the long run.

What We’re Building

• Foundational Ranking Backbone Models: Multi-task/multi-objective models (shared encoders + task heads) that jointly learn relevance, conversion, margin contribution, churn risk, and ad quality, enabling consistent decisions across search and recommendations.
• Value-Aware Optimization: Uplift and long-horizon value models that steer decisions toward incrementality and LTV, with calibrated constraints on quality, diversity, fairness, and spend pacing—plus guardrails for safe exploration.
• LLM-Enhanced Retrieval & Features: Using LLMs to enrich query and item semantics for long-tail recall, generate features for cold-starts, and feed the ranker with reasoning-rich context, while remaining the source of truth for final ordering.

Our commitment to AI innovation is reflected in our recent publications and research contributions to the field.

About the Job

• Architect the ranking backbone that unifies query understanding, personalization, multi-objective ranking, ads, and merchandising into a single adaptive platform.
• Design and build a search autosuggest system optimized for personalization and value-based relevance.
• Design long-horizon objective functions (e.g., incrementality, LTV, habit formation) and build uplift/causal value models that move beyond short-term engagement.
• Develop production-grade Multi-Task Learning (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk—ensuring calibration, constraints, and explainability.
• Own the inference layer: goal-aware re-rankers, diversity and quality constraints, safe exploration, and millisecond-class latency optimization.
• Advance evaluation practices: online experiments, long-horizon cohort metrics, counterfactual evaluations, and attribution pipelines for tracking incremental GTV and retention.
• Partner across ads, infrastructure, product, and design 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.

About You
Minimum Qualifications

• 5+ years applying ML at scale (3+ years in technical leadership), with a proven track record improving ranking or recommendation systems in production.
• Demonstrated success in applying multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience; experience with online testing and attribution beyond CTR.
• Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch).
• Excellent analytical skills and strong cross-functional communication abilities.

Preferred Qualifications

• Expertise in multi-task learning architectures (e.g., MMOE/PLE, shared encoders), calibration, counterfactual evaluation, uplift/causal modeling, and/or contextual bandits for exploration.
• Experience building low-latency ranking services, including feature stores, caching, vector + lexical retrieval, re-ranking, and A/B testing infrastructure, with expertise in constraint-aware inference.
• Hands-on experience with LLMs as feature/recall enhancers (e.g., embeddings, adapter tuning) while maintaining clarity on when the ranker should arbitrate.

Overview

About the Role:

We are hiring a tech lead who will drive major initiatives, identify impactful opportunities, define roadmaps, mentor team members, and develop ML/AI solutions in the incentive space to drive substantial business growth.

We are the ML team within Instacart’s Growth org - a fast-moving, results-driven group building production-ready ML/AI systems to optimize incentive allocation. We tackle unique, high-impact problems, value bottom-up ideas, and partner closely with Product, Marketing, Data Science, and Backend Engineering to drive growth.

About the Job:

• Identifying high-leverage opportunities in incentive optimization, defining the technical roadmap, and driving end-to-end execution of ML/AI solutions to deliver measurable business impact.

• Partnering closely with product managers, marketers, data scientists, and backend engineers to deeply understand business needs and build impactful ML applications. 

• Leading and mentoring the team, raising technical standards, and improving team execution efficiency.

About You:

Minimum Qualifications:

• Proven track record of identifying high-impact opportunities, defining roadmaps, driving major initiatives, partnering with cross-functional stakeholders, and hands-on developing production-ready ML/AI solutions that deliver measurable business results.
• Strong skills in analytics, modeling, programming, and communication
• Strong ownership, results-driven, and continuously innovating to deliver impact.

Preferred Qualifications:

• Experience in incentive or pricing optimization. 
• Experience in causal modeling.
• Experience in LLM and reinforcement learning.