ML Engineer

We’re looking for a Senior Machine Learning Engineer to join a remote role within a Grocery Delivery organization. In this role you’ll build ML models to analyze shopper behavior when considering pricing and promotions.

We're seeking mid-level Machine Learning Engineers with a solid grasp of core ML concepts to join our dynamic team. Here, you'll tackle exciting and complex challenges—whether it's optimizing delivery routes to ensure timely arrivals or helping customers discover the perfect items. Machine learning is the driving force behind these innovations, playing a pivotal role in Instacart’s bold growth strategy and shaping our vision for the future.

• 4+ years of experience in Data Science or Machine Learning, with a clear track record of driving business value
• B.S., M.S., or PhD in Statistics, Mathematics, Operations Research, Machine Learning, Economics, Computer Science, or related quantitative fields
• Strong understanding of Generalized Linear Models and advanced statistics
• Proficient in Python, with hands-on experience using libraries like NumPy and Pandas
• Experience in demand modeling, pricing strategies, and causal inference methods

• Startup experience is a plus
• Experience in Hypothesis Testing, Maximum Likelihood Estimation, and A/B testing
• Strong foundation in math and statistics, including Linear Algebra, Bayesian Methods, and Calculus
• Expertise in Machine Learning techniques like GBDT, Random Forests, Neural Networks, Regularization Methods, and Dimensionality Reduction
• Familiarity with Agile methodologies and processes

• Research and implement cutting-edge Data Science and Machine Learning models to optimize pricing strategies and shopper behavior analysis
• Build models that measure the impact of price changes and promotions, balancing modern AI methods with proven classical techniques
• Develop systems to optimize pricing and experiment design across hundreds of stores and thousands of products, while managing the exploration-exploitation tradeoff
• Design automated pricing experiments, run them at scale, and analyze results to continually improve models