Machine Learning Engineer

Who you are

• Have a graduate degree (masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field
• Have strong programming skills and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (classical ML and Deep Learning) tools
• Have strong analytical skills and problem-solving ability
• Are a strong communicator who can collaborate with diverse stakeholders across all levels
• Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets
• Knowledge of sequential modeling, Transformer architecture and Foundation Model
• Familiarity with LLM integrations, agentic workflow and productivity tooling
• Experience in building large scale online recommendation systems

What the job involves

• 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, bidding, and auction across all of Instacart’s consumer facing surfaces and Ads Ecosystems
• You will actively contribute to initiatives, assisting in all stages of ML projects from the initial concept, through prototyping and experimentation, to final launch
• The Ads Response Prediction team owns 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, LLM integrations, relevance models, pCTR models, bidding models and Foundation 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
• Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., miss-calibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria
• Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications
• Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models
• Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives