Machine Learning PhD Intern, Economics
InstacartUnited States - RemotePosted 24 February 2026
Job Description
<div class="content-intro"><p><strong>We're transforming the grocery industry</strong></p>
<p><span class="im">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.</span></p>
<p>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.</p>
<p><strong>Instacart is a Flex First team </strong></p>
<p>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. <a href="https://instacart.careers/remote/" target="_blank" data-saferedirecturl="https://www.google.com/url?q=https://instacart.careers/remote/source=gmailust=1651869232122000usg=AOvVaw37OlxP8hAKN7nq4YwHQH7e">Learn more about our flexible approach to where we work.</a></p></div><p> </p>
<p><strong>Overview</strong></p>
<p>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.</p>
<p>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 <a href="https://tech.instacart.com/the-economics-team-at-instacart-94c48db951e8">blog post</a> that introduces the team and the type of work we do.</p>
<p> </p>
<p><strong>About the Job</strong></p>
<ul>
<li>You will help design and build end-to-end machine learning solutions.</li>
<li>You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects.</li>
<li>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.</li>
<li>You will develop high impact solutions to support Instacart's ambitious growth plans.</li>
<li>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.</li>
<li>You will have the freedom to suggest and drive organization-wide initiatives.</li>
</ul>
<p> </p>
<p><strong>About You</strong></p>
<p>Minimum Qualifications</p>
<ul>
<li>Current or recently graduated PhD student in economics or a related field with focus on data-intense problems.</li>
<li>A blend of economic theory, applied econometrics, and business acumen that let you jump into a fast-p ... (truncated, view full listing at source)
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