Senior Machine Learning Engineer, Fulfillment

Instacart

9d•$173,000 - $218,500•Remote

About The Position

Instacart’s Enterprise AI organization builds intelligent products and platforms using machine learning, optimization, and software engineering to address complex problems across its business. The Senior Machine Learning Engineer designs, builds, and deploys production machine learning systems that create measurable business impact. The role spans the full machine learning product lifecycle, from problem formulation and data development through experimentation, deployment, monitoring, and continuous improvement. The engineer works closely with product managers, data scientists, platform engineers, and business partners to turn ambiguous opportunities into reliable, scalable solutions. This is a fast-moving, highly collaborative environment where priorities, data, and business needs can evolve. Success calls for comfort with ambiguity, pragmatic prioritization, sound technical judgment, ownership from idea through production, and hands-on implementation. The role is remote under Instacart’s Flex First approach. Employees may choose to work from home, an office, or another suitable location, while participating in regular in-person events. The position offers a location-dependent base pay range and is eligible for new-hire equity and annual refresh grants.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
Five or more years of professional experience developing and deploying machine learning models or systems in production.
Proficiency in Python and SQL, including production-quality coding, automated testing, profiling, debugging, and code review practices.
Experience across the machine learning lifecycle, including data preparation, feature engineering, model evaluation, experimentation, deployment, and production monitoring.
Experience building and operating machine learning services or data products in a cloud environment such as Google Cloud Platform or Amazon Web Services.
Experience with containerized services and production software development practices, including Docker, continuous integration, monitoring, alerting, and incident response.
Ability to work with product, engineering, data science, and business stakeholders to define requirements and deliver measurable outcomes.

Nice To Haves

Master’s or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
Experience designing machine learning systems for large-scale, high-throughput, or low-latency applications.
Familiarity with distributed data processing, streaming systems, feature stores, or real-time feature computation.
Experience with model serving, orchestration, and machine learning platforms such as Kubernetes, Airflow, Spark, or comparable technologies.
Experience designing simulation, experimentation, or counterfactual evaluation frameworks for machine learning and decision systems.
Proficiency in a performance-oriented language such as C++, Java, or Go.
Experience mentoring engineers, leading cross-functional projects, or setting technical direction for machine learning initiatives.
Experience working in e-commerce, marketplaces, logistics, retail, advertising, or another domain involving complex and evolving business constraints.

Responsibilities

Design, implement, and deploy machine learning models and services for Enterprise AI products and business initiatives.
Own the machine learning lifecycle, including problem formulation, data preparation, feature development, model training, offline evaluation, experimentation, production rollout, and ongoing performance monitoring.
Build reliable, scalable, maintainable software in Python and related technologies, using testing, profiling, code review, documentation, and operational-readiness practices.
Partner with product, engineering, data science, and business stakeholders to define success metrics, prioritize work, and deliver solutions that improve customer and business outcomes.
Develop and operate machine learning services on cloud infrastructure, with monitoring, alerting, observability, and safeguards for production performance and reliability.
Use experimentation, statistical analysis, and causal or counterfactual evaluation techniques to assess model quality and guide launch decisions.
Help shape technical direction through design reviews, architecture discussions, and evaluation of modeling approaches, infrastructure choices, and tradeoffs.
Share knowledge, mentor teammates, improve development practices, and participate in operational support for critical services.