• Data ecosystem onboarding: Get familiar with how our services, databases, and data warehouse are structured and interact. • Data quality contribution: Start contributing to data quality initiatives like the Talent Graph (our version of a knowledge graph, mapping relationships between projects in Float with people and their attributes). Your focus will be on making our data more accurate, actionable, and future-ready. • CDC initiative support: Join CDC-related efforts including real-time transformations and implementing Data Contracts for improved consistency and maintainability. • CDC ownership and evolution: Take the lead on CDC-related initiatives, contributing to long-term planning and system resilience across our platform. • Platform-driven data quality leadership: Own data quality initiatives from the platform side, introducing observability, reducing regressions, and improving trust in downstream data. • Data warehouse maintenance and support: Help maintain and evolve our analytics data warehouse, supporting migrations and performance improvements in collaboration with the Data team. • Architectural improvement proposals: Identify and drive improvements to our data architecture, focusing on scalability, consistency, and future platform needs.

• FBS – Farmer Business Services is part of Farmers operations to build a global approach to identifying, recruiting, hiring, and retaining top talent. • Deliver consistent and sustainable results by building diverse and high-performing teams. • A solid and innovative company with a strong market presence. • Continuous learning and development.

• You'll own the APIs, data pipelines, and workflow orchestration that power our AI products—from real-time model inference to long-running optimization pipelines. • This role sits at the intersection of backend engineering and data engineering: you'll build the services that serve up models, manage workflows, and connect AI capabilities to the structured data that makes them useful. • You'll work closely with our Active Learning and LLM/Agents team leads, translating their product vision into scalable, production-grade systems. • The infrastructure you build will power model playgrounds for chemists, inverse design pipelines that optimize experiments across high-dimensional spaces, and orchestrated agent workflows that reason through complex scientific problems. • Design and build high-performance Python APIs that serve models, manage workflows, and expose AI capabilities to the broader platform • Architect backend services for scalability, reliability, and low latency • Build integrations between AI/ML systems, graph databases, and external data sources • Build and maintain long-running workflow pipelines using Ray and Temporal. • Design orchestration patterns for multi-step agent pipelines, batch inference, and numerical optimization workflows • Ensure fault tolerance, graceful degradation, and efficient resource utilization. • Architect and maintain data pipelines that feed AI/ML workflows • Work with Neptune (graph), Redis, DynamoDB, and other data stores to enable efficient data access patterns • Implement observability including logging, metrics, tracing, and alerting • Own system reliability—troubleshoot issues, conduct post-mortems, and continuously improve. • Design CI/CD pipelines and promote automation best practices.

• You will be instrumental in elevating our core ML platform to its next level of performance, reliability, and scalability. • You'll work on the critical infrastructure that directly enables all of Afresh's Machine Learning and Applied Science teams to innovate faster and deliver impact. • Your contributions will empower our product suite, including our flagship Prediction Engine, to power replenishment decisions on more than 15% of all produce sold in the United States. • In your first 3 months, you might deliver a feature that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems. • By the end of your first 6 months, you will have owned the implementation of significant scalability improvements and additions to our ML platform.