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 machine learning engineer in the Economics team, you will build state-of-the-art systems that blend rigorous economic thought with sophisticated machine learning algorithms to tackle some of the company's most challenging problems. Working in a horizontal team, you will have the opportunity to collaborate closely with partners across multiple functions to operate on a highly diverse set of problems, contributing both economic and engineering expertise in a fast-paced environment filled with exciting opportunities for technically-minded economists.
The Economics team at Instacart works on a range of interesting and challenging problems across our platform, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making. Some of the core areas of focus for our team include matching and logistics, online advertising, uplift and long-term value modeling, and general causal inference. Check out this blog post to learn more.
About the Job
- Design, develop, and deploy machine learning solutions to tackle the many economic challenges in our complex marketplace.
- Collaborate closely with product managers, data scientists, and other engineers to deeply understand business needs and create impactful solutions.
- Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.
- Be an active member of the Economics team, sharing learnings, best practices, and research across domains while refining and advancing our algorithms.
About You
We are open to hiring either a Machine Learning Engineer II, Economist (fresh PhD graduate) or a Senior Machine Learning Engineer I, Economist (post-PhD industry experience).
Minimum Qualifications
- Graduate Degree (Masters or PhD) in Economics or a closely related field
- A blend of economic theory, applied econometrics, and business skills that let you jump into a fast-paced environment and contribute from day one.
- Experience applying causal inference methodologies to both observational and experimental datasets.
- An understanding of machine learning algorithms and techniques.
- Strong programming skills (Python) and fluency in data manipulation (SQL, Pandas) and machine learning tools (e.g., scikit-learn, XGBoost).
- Excellent communication skills (both verbal and written).
- Self-motivation and a strong sense of ownership.
For Senior Machine Learning Engineer I, Economist hires, we also expect:
- 1-3 years of industry experience in a similar position.
- Experience putting machine learning models into production environments.
- Experience with cloud computing and related ML infrastructure.
Preferred Qualifications
- A PhD in Economics or a closely related field with a focus on data-intense problems.
- Intern experience in related roles.
- Experience with large language models and generative AI, both on the algorithm side as well as a day-to-day tool.
- Experience with uplift modeling, contextual bandits, and/or heterogeneous treatment effect estimation.
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