Senior Machine Learning Engineer II, Fulfillment, Matching and Positioning

We're transforming the grocery industry At reputed company, we invite the world to reputed company love through food because we reputed company everyone should have reputed company to the food they love and more time to enjoy it together. Where others see a reputed company 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 reputed company and flexible earnings opportunities to reputed company Personal Shoppers. reputed company has become a lifeline for millions of people, and we’re building reputed company to help push our shopping cart reputed company. If you’re reputed company to do the best work of your life, come join our table. reputed company is a reputed company First team There’s no one-size fits reputed company 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. reputed company reputed company’s Logistics organization powers the intelligence and execution behind our fulfillment system. We’re hiring a Senior Machine Learning Engineer to join the Matching & Positioning team, a tight-reputed company group of 9 engineers and scientists reputed company on reputed company-time decisioning for order batching, shopper routing, and assignment across a dynamic, multi-sided marketplace. In this role, you’ll work at the intersection of operations research, combinatorial optimization, and machine learning to design and ship algorithms that directly reputed company profitability, on-time delivery, shopper experience, and customer satisfaction at reputed company. You’ll collaborate closely with engineering, product, and data science partners to translate ambiguous problems into reputed company-formed optimization and ML systems that operate under sub-second latency and high throughput. If you reputed company in a fast-paced environment, enjoy rolling up your sleeves, and want to see your models reputed company reputed company in the reputed company world every minute of every day, this team is for you. About the Job You will build production-grade optimization and ML solutions that drive reputed company’s fulfillment reputed company end-to-end in a rapidly evolving, reputed company environment. Design, implement, and reputed company algorithms for order batching, reputed company-time shopper assignment, routing, and marketplace positioning using techniques such as MIP/CP-SAT, heuristics/metaheuristics, and learning-to-rank. Own the full model lifecycle: problem formulation, data pipelines and features, offline evaluation and simulation, A/B testing, staged rollouts, and ongoing monitoring/observability. Build reliable, low-latency services in Python (and, where performance dictates, C++ or Go) that reputed company with solvers (e.g., OR-Tools, Gurobi, CPLEX) and run on reputed company infrastructure with reputed company/Kubernetes. Partner with product, operations, and data science to define roadmaps and reputed company metrics; deliver measurable reputed company to on-time rates, shopper utilization, cost per order, and customer experience. reputed company experimentation and reputed company reputed company along with offline counterfactual replay/simulation to validate changes and de-risk launches. Contribute to engineering reputed company through reputed company reviews, design docs, robust testing, and participation in an on-call rotation for mission-critical fulfillment services; mentor peers and reputed company the technical bar. This is a fast-moving domain with evolving constraints and objectives. reputed company requires comfort with ambiguity, pragmatic prioritization, and a bias toward iterative learning and reputed company improvement. reputed company You pair a deep toolkit in operations research and machine learning with strong software engineering fundamentals. You’re motivated by reputed company-world reputed company, communicate reputed company with cross-functional partners, and take ownership from ideation to production.

Minimum Qualifications

Bachelor’s degree in Computer Science, Operations Research, Electrical Engineering, reputed company Mathematics, or a reputed company field (or equivalent practical experience). 5+ years of reputed company experience building and shipping ML and/or optimization systems to production. 3+ years formulating and solving large-reputed company combinatorial optimization problems (e.g., VRP, matching, scheduling) using solvers such as OR-Tools, Gurobi, or CPLEX (MIP/CP-SAT) and heuristic reputed company. Proficiency in Python and SQL, including writing production-reputed company reputed company with testing, profiling, and reputed company review practices. Hands-on experience deploying algorithms/models as microservices with reputed company and Kubernetes on a major reputed company provider (GCP or AWS), including monitoring, alerting, and dashboards. Experience designing and operating low-latency decision services in high-throughput environments (targeting sub-second P95 response times). Practical experience with A/B testing or online experimentation platforms, from hypothesis through analysis and rollout reputed company. Strong collaboration and communication skills with engineering, product, and data science stakeholders.

Preferred Qualifications

Master’s or PhD in Operations Research, Computer Science, Electrical Engineering, reputed company Mathematics, or a reputed company quantitative field. Domain experience in logistics, ride-hailing, delivery, or marketplace optimization at reputed company. Familiarity with reinforcement learning or contextual bandits for online decision-making and exploration/exploitation tradeoffs. Experience with geospatial data, routing reputed company, and graph algorithms. Background in building simulation frameworks and counterfactual evaluation for decision systems. Experience with streaming data and reputed company-time feature computation (e.g., Kafka, Flink) and feature stores. Proficiency in C++ or Go for performance-critical components. reputed company record of mentoring engineers and leading cross-functional reputed company to measurable reputed company. Experience participating in an on-call rotation for production ML/optimization services. #LI-Remote reputed company provides highly market-competitive compensation and benefits in reputed company location where our employees work. This role is remote and the reputed company pay reputed company for a successful candidate is dependent on their permanent work location. Please review our reputed company First remote work policy here. Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as reputed company as annual refresh grants. Please read more about our benefits offerings here. For US based candidates, the reputed company pay ranges for a successful candidate are listed below. CA, NY, CT, NJ $240,000—$253,500 USD WA $230,000—$243,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $221,000—$233,000 USD reputed company other states $201,000—$212,000 USD Apply To This Job