ML Engineer II - Remote

Instacart is seeking a Machine Learning Engineer II to help develop and improve the intelligence behind its logistics and marketplace systems. In this role, you will collaborate with senior engineers, product leaders, product managers, data scientists, and backend engineers to apply machine learning to complex problems involving routing optimization, pricing, dispatch, mapping, matching, and geospatial systems., * Design, develop, and deploy machine learning solutions for practical marketplace and logistics challenges.

• Develop and refine algorithms and machine learning models supporting Instacart’s marketplace systems.
• Apply machine learning to areas such as routing optimization, pricing, dispatch, mapping, matching, and geospatial problems.
• Collaborate with product managers, data scientists, backend engineers, senior engineers, and product leaders to understand business requirements and develop impactful ML/AI applications.
• Participate in the full solution lifecycle, including concept development, prototyping, experimentation, implementation, and refinement.
• Work with diverse stakeholders to ensure machine learning solutions are effectively integrated and aligned with business goals.
• Continually improve algorithms and models to advance operational efficiency.
• Work with large and dynamic datasets within high-throughput marketplace systems., * Base pay varies according to the candidate’s permanent work location.
• The role is eligible for a new-hire equity grant.
• The role is eligible for annual equity refresh grants.
• Additional benefits are available according to Instacart’s applicable benefits offerings.

Base salary range: The provided job description states that U.S. base pay ranges are listed separately but does not include the specific range in the supplied content.

Compliance / Additional Information

The role is part of Instacart’s Logistics AI group, which focuses on fulfillment intelligence and execution across a multi-sided marketplace.

Instacart’s Flex First approach provides flexibility in where employees work while supporting connection and community through regular in-person events.

You will contribute across the full machine learning lifecycle, from understanding business needs and developing initial concepts through prototyping, experimentation, deployment, and ongoing optimization. The successful candidate will combine strong machine learning and programming skills with analytical problem-solving and effective cross-functional communication., * Graduate degree (Master’s or PhD) in Artificial Intelligence, Machine Learning, Operations Research, or equivalent self-study and experience.

• Strong programming skills in Python.
• Fluency in data manipulation using SQL and Pandas.
• Experience with machine learning tools and frameworks such as scikit-learn, XGBoost, Keras, and/or TensorFlow.
• Strong analytical and problem-solving skills.
• Strong communication skills and ability to collaborate with diverse stakeholders across different levels., * 1-2 years of industry experience applying machine learning to real-world problems involving large datasets.
• Knowledge of deep learning frameworks and methodologies.
• Experience applying machine learning and optimization techniques to marketplace problems.

Skills & Competencies

• Machine Learning
• Artificial Intelligence
• Python
• SQL
• Pandas
• scikit-learn
• XGBoost
• Keras
• TensorFlow
• Data manipulation
• Machine learning model development
• Algorithm development and optimization
• Deep learning
• Machine learning experimentation
• Routing optimization
• Pricing and dispatch optimization
• Mapping and geospatial applications
• Analytical thinking
• Problem-solving
• Cross-functional collaboration
• Stakeholder communication
• Operational efficiency

Education & Experience

Education:

Graduate degree (Master’s or PhD) in Artificial Intelligence, Machine Learning, Operations Research, or equivalent self-study and experience.

Experience:

No specific industry experience is required as a minimum qualification. Preferred candidates may have 1-2 years of industry experience applying machine learning to real-world problems with large datasets.

Work Arrangement & Schedule