Machine Learning Engineer II, Ads - Response Prediction

Instacart

9d•CA$154,000 - CA$162,500•Remote

About The Position

We're transforming the grocery industry at Instacart, inviting the world to share love through food. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.