
Instacart · United States - Remote
We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food...
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love
and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless
opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get
their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If
you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their
best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through
regular in-person events. Learn more about our flexible approach to where we work.
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.
create impactful ML applications.
XGBoost, Keras/Tensorflow) tools
#LI-Remote
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is
remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex
First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is
eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
WA
All other states
We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. 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). #LI-Remote Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here. For US based candidates, the base pay ranges for a successful candidate are listed below. CA, NY, CT, NJ $240,000—$253,500 USD WA $230,000—$243,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $221,000—$233,000 USD All other states $201,000—$212,000 USD
We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. 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. Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here. For US based candidates, the base pay ranges for a successful candidate are listed below. CA, NY, CT, NJ $240,000—$253,500 USD WA $230,000—$243,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $221,000—$233,000 USD All other states $201,000—$212,000 USD
Relay is fundamentally reshaping how goods move in an online era. Backed by Europe’s largest-ever logistics Series A ($35M), led by deep-tech investors Plural (whose portfolio spans fusion energy and space exploration), Relay is scaling faster than 99.98% of venture-backed startups. We're assembling the most talent-dense team the logistics industry has ever seen Relay’s Mission is to free commerce from friction. Today, high delivery costs act as a hidden tax on e-commerce, quietly shaping what can be sold online and limiting who can participate. We envision a world where more goods move more freely between more people, making the online shopping experience seamless and accessible to everyone. THE TEAM • ~110 people, more than half in engineering, product and data • 45+ advanced degrees across computer science, mathematics and operations research • Thousands of data points captured, calculated, analysed and predicted for every single parcel we handle • An intellectually vibrant culture of first‑principles thinking, tight feedback loops and relentless experimentation Every parcel Relay handles is touched by ML. We recommend and optimise route assignment, predict delivery durations, estimate parcel dimensions and weight, detect objects in images on device, forecast demand and decide network handovers. That's 10+ models running in the critical path of a live logistics network where quality is non-negotiable. ML Stack Highlights * Python and Rust. We keep things simple but use the right tool for the job * Rust with ONNX in-process model execution where throughput is critical * Chalk.ai as our Feature Store * GCP Agent Platform Endpoints for model serving * Cloud-native on GCP. Services run on Kubernetes, with extensive use of BigQuery The Opportunity As a Senior Machine Learning Engineer at Relay, you'll: * Own critical part of ML: productionising of our models end-to-end, from training pipeline through live integration to measured business impact. * Build and mature our ML Platform. Evolve the model serving architecture, expand reusable components adoption and set the standards for how Relay ships ML org-wide. * Strengthen existing models by architecting integration and system testing within training pipelines, automated releases and drift monitoring. * Launch completely revamped processes side by side with data scientists and measure their real-world impact on the network. We're looking for candidates who… * Have at least two years deploying and operating models in production and four years building software on high-performing teams. * Are comfortable diving into unfamiliar codebases and languages to ship a model into a live system. * Prefer building the automation that removes manual labour over repeating it. Who Thrives at Relay? * Aim with Precision: You define problems clearly and measure your impact meticulously. * Play to Win: You chase bold bets, tackle the hard stuff, and view constraints as fuel, not friction. * 1% Better Every Day: You believe that small, consistent improvements lead to exponential growth. You move quickly, deliver results, and learn from every experience. * All In, All the Time: You show up and step up. You take ownership from start to finish and do what it takes to deliver when it counts. * People-Powered Greatness: You invest in your teammates. You give and receive feedback with care and candour. You build trust through high standards and shared success. * Grow the Whole Pie: You seek out win-win solutions for merchants, couriers, and our customers, because when they thrive, so do we. If these resonate, and you combine strong technical fundamentals with entrepreneurial drive, let’s connect. Relay is an equal-opportunity employer committed to diversity, inclusion, and fostering a workplace where everyone thrives.