Senior Machine Learning Engineer, Fulfillment

Description

The company runs a grocery delivery service that customers rely on for food and household goods, and it gives personal shoppers flexible ways to earn.

The Enterprise AI group builds intelligent products and platforms for customers, shoppers, retailers, brands, and internal teams, using machine learning, optimization, and software engineering.

This role sits on a team that designs, builds, and ships production machine learning systems with measurable business value, covering the whole product lifecycle from defining the problem to continuous improvement.

You will work with product managers, data scientists, platform engineers, and business partners to turn loosely defined opportunities into reliable, scalable solutions. Expect a fast-moving setting where priorities and data can shift, and where your systems reach real users at large scale.

The ideal candidate pairs solid machine learning depth with strong software engineering fundamentals and wants ownership from idea to production.

Responsibilities

• Design, build, and ship machine learning models and services that support major Enterprise AI products and business initiatives.
• Take charge of the whole ML lifecycle: framing the problem, preparing data, building features, training, offline evaluation, experiments, rollout, and monitoring over time.
• Write maintainable Python and related code, with testing, profiling, code review, documentation, and production readiness.
• Work with product, engineering, data science, and business partners to set success metrics, set priorities, and deliver results that improve customer and business outcomes.
• Run machine learning services on cloud infrastructure with monitoring, alerting, observability, and safeguards for performance and reliability.
• Apply experimentation, statistical analysis, and causal or counterfactual methods to judge model quality and inform launch decisions.
• Shape technical direction through design reviews, architecture discussions, and clear evaluation of modeling and infrastructure tradeoffs.
• Grow the engineering culture by sharing knowledge, mentoring peers, improving practices, and supporting critical services in operations.
• Move between high-level problem solving and hands-on implementation as priorities and needs change.

Requirements

• Minimum: a bachelor's degree in computer science, engineering, mathematics, statistics, or a related technical field, or equivalent practical experience.
• Minimum: 5 or more years of professional experience building and deploying machine learning models or systems in production.
• Minimum: proficiency in Python and SQL, with production-quality code, automated testing, profiling, debugging, and code review.
• Minimum: experience across the full ML lifecycle, from data preparation and feature engineering to evaluation, experiments, deployment, and production monitoring.
• Minimum: experience building and running ML services or data products on a cloud platform such as Google Cloud Platform or Amazon Web Services.
• Minimum: experience with containers such as Docker, continuous integration, monitoring, alerting, and incident response.
• Minimum: a record of working with product, engineering, data science, and business teams to define requirements and deliver measurable results.
• Preferred: a master's or PhD in computer science, engineering, mathematics, statistics, or a related quantitative field.
• Preferred: experience designing ML systems for large-scale, high-throughput, or low-latency use.
• Preferred: familiarity with distributed data processing, streaming systems, feature stores, or real-time feature computation.
• Preferred: experience with model serving and orchestration on platforms such as Kubernetes, Airflow, Spark, or similar.
• Preferred: experience building simulation, experimentation, or counterfactual evaluation frameworks for ML and decision systems.
• Preferred: proficiency in a performance-oriented language such as C++, Java, or Go.
• Preferred: experience mentoring engineers, leading cross-functional projects, or setting technical direction for ML work.
• Preferred: experience in e-commerce, marketplaces, logistics, retail, advertising, or another domain with complex, changing business constraints.

Conditions

This is a remote role under the company's Flex First model, where you choose where you do your best work, such as home, an office, or a café, with regular in-person events.

Base pay depends on your permanent work location in the US. Ranges by location: CA, NY, CT, NJ: $207,000 to $218,500 USD.

WA: $198,000 to $209,000 USD.

OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI: $190,000 to $200,500 USD.

All other states: $173,000 to $182,500 USD.

Offers may vary based on factors such as your experience and the skills the role requires.

Benefits

• Pay and benefits are set to be market competitive for each work location.
• Eligible for a new hire equity grant and annual refresh grants.