Machine Learning Engineer, Instacart

• Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, recommendation systems or equivalent self study and experience
• Have strong programming skills in Python and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (scikit-learn, XGBoost) and Deep Learning (Torch, Keras/Tensorflow) tools. Acquaintance or willing to learn GoLang
• Have strong analytical skills and problem-solving ability
• Are a strong communicator who can collaborate with diverse stakeholders across all levels

Desirable

• As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team
• Together, you'll develop and enhance Instacart's ads systems
• You will use machine learning to devise and refine solutions in crucial areas such as ads selection, ranking, auction, and pricing across all of Instacart’s consumer facing pages like Search, storefront, aisle, pre and post checkout experiences, among many others
• You will actively contribute to initiatives, assisting in all stages from the initial concept, through prototyping and experimentation, to final implementation
• The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart
• This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models
• The team optimizes for an efficient marketplace to ensure customers see ads that help them try new products/brands, advertisers boost their product sales for a good return on investment, and instacart generates the deserving revenue as well
• Design, develop, and deploy machine learning solutions including data pipelines, model architectures and serving integrations to tackle practical challenges in the ads organization
• Collaborate closely with product managers, data scientists, and infrastructure 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