Machine Learning Engineer, Operations Research

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.

• 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

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.

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.