Senior Machine Learning Engineer, Economist

Instacart is transforming grocery delivery and building technology for its complex, multi-sided marketplace. The Economics team seeks a machine learning engineer to design, deploy, and refine machine learning systems that address economic challenges while collaborating with product managers, data scientists, and engineers.

Responsibilities:

• 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

Requirements:

• 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
• 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
• 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