Part-Time Student Worker AI & Automation: Supply Chain, Quality & Reliability

The Supply Chain, Quality & Reliability (SCQR) organization at Zoox builds and manages the external ecosystem and internal safeguards that allow Zoox to build and operate. We secure the third-party technologies, goods, and services Zoox depends on; define how Zoox engages with its global supplier and vendor base; embed reliability into the vehicle from early concept through long-term fleet operations; steward the Zoox Quality Policy and culture from the smallest supplier part to the rider experience; guarantee the health of the physical infrastructure that supports our fleet at scale; drive Zoox's sustainability strategy toward our Amazon Climate Pledge Net Zero 2040 commitment; manage third-party risk and supplier resilience; and safeguard Zoox's reputation through ethical, compliant, and strategic commercial engagement with the outside world. As Zoox scales toward commercial deployment, the volume and complexity of supplier, quality, reliability, infrastructure, and procurement data are growing rapidly across all of these areas, and we are investing in AI to help our team work smarter, faster, and more proactively across the full SCQR mandate. Zoox is launching a dedicated AI & Automation initiative within SCQR, made up of 3-4 co-op interns working together as a centralized team that partners with our SCQR leaders and their teams to identify, build, and deploy AI-driven solutions across the organization. The AI & Automation Co-op Intern will join this pod and contribute to one of the most exciting AI adoption initiatives at Zoox  building agentic systems, RAG pipelines, and MCP integrations against real SCQR data, and coaching the SCQR team to extend and maintain these capabilities long after the co-op term ends. The pod reports directly into SCQR leadership to ensure strategic focus, executive visibility, and access across the broader management team. $30 per hour. This is a contract position and employment for this position will be through a vendor contracted with Zoox. The hourly pay range is posted and you will be eligible for a benefits package as offered by the vendor. In This Role You Will Partner with SCQR leaders and team members workflows and identify high-leverage opportunities where AI can eliminate toil, accelerate decisions, and surface insights the team would otherwise miss Design and build AI agents, RAG pipelines, and MCP server integrations that work against Zoox's real SCQR data supplier scorecards, non-conformance reports, audit findings, CAPA responses, inspection records, reliability signals, infrastructure readiness data, contracts, and procurement transactions Ship production-ready tools that the SCQR team will actually adopt and use in their daily work, not proofs of concept that die in a notebook Run hands-on coaching sessions, office hours, and documentation efforts so SCQR team members can extend, maintain, and build on what you've shipped after your co-op term ends Develop reusable components, patterns, and playbooks so that each intern's work compounds across the pod and across future co-op cohorts Present progress, measured impact, and forward priorities to SCQR leadership on a regular cadence Qualifications Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related discipline, and enrolled in a co-op program Able to commit to a minimum of a 12-week co-op term Demonstrated, hands-on experience building agentic AI systems: function calling, tool use, multi-step planning, and orchestration Hands-on experience with at least two of the following: Retrieval-Augmented Generation (RAG) (vector stores, retrieval strategies, evaluation), fine-tuning (LoRA, SFT, or similar), Model Context Protocol (MCP) server development, prompt engineering at production scale Fluency in Python and direct experience working with at least one LLM provider API (Anthropic, OpenAI, or similar) Strong written and verbal communication skills, with the ability to explain technical work clearly to non-technical engineering, operations, and quality stakeholders A portfolio or write-up of at least one agentic system you have built end-to-end open source, prior internship, or personal project all welcome Bonus Qualifications Prior exposure to manufacturing, supply chain, supplier quality, procurement, reliability engineering, or quality management systems Experience shipping AI-powered tools that were used by real people beyond yourself (open source projects with users, internal tools at prior internships, side projects with adoption) Experience with LLM evaluations, observability, guardrails, or safety tooling Familiarity with enterprise data platforms (e.g., Snowflake, Databricks), business systems (e.g., SAP, Oracle), or quality management systems Experience working in a cross-functional environment partnering with non-technical teams Program Requirements Currently pursuing a B.S. or M.S., in a relevant engineering field Available for a 6 month project Able to commit to at least 20 hours per week Ability to commute on-site to one of our offices Student Worker may not use proprietary Zoox information in university theses, publications, or share it outside

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      "text": "In This Role You Will ",
      "content": "<div>\n\n<li>Partner with SCQR leaders and team members workflows and identify high-leverage opportunities where AI can eliminate toil, accelerate decisions, and surface insights the team would otherwise miss</li>\n<li>Design and build AI agents, RAG pipelines, and MCP server integrations that work against Zoox's real SCQR data&nbsp; supplier scorecards, non-conformance reports, audit findings, CAPA responses, inspection records, reliability signals, infrastructure readiness data, contracts, and procurement transactions</li>\n<li>Ship production-ready tools that the SCQR team will actually adopt and use in their daily work, not proofs of concept that die in a notebook</li>\n<li>Run hands-on coaching sessions, office hours, and documentation efforts so SCQR team members can extend, maintain, and build on what you've shipped after your co-op term ends</li>\n<li>Develop reusable components, patterns, and playbooks so that each intern's work compounds across the pod and across future co-op cohorts</li>\n<li>Present progress, measured impact, and forward priorities to SCQR leadership on a regular cadence</li>\n\n</div>"
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      "text": "Qualifications ",
      "content": "<div>\n\n<li>Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related discipline, and enrolled in a co-op program</li>\n<li>Able to commit to a minimum of a 12-week co-op term</li>\n<li>Demonstrated, hands-on experience building agentic AI systems: function calling, tool use, multi-step planning, and orchestration</li>\n<li>Hands-on experience with at least two of the following: Retrieval-Augmented Generation (RAG) (vector stores, retrieval strategies, evaluation), fine-tuning (LoRA, SFT, or similar), Model Context Protocol (MCP) server development, prompt engineering at production scale</li>\n<li>Fluency in Python and direct experience working with at least one LLM provider API (Anthropic, OpenAI, or similar)</li>\n<li>Strong written and verbal communication skills, with the ability to explain technical work clearly to non-technical engineering, operations, and quality stakeholders</li>\n<li>A portfolio or write-up of at least one agentic system you have built end-to-end&nbsp; open source, prior internship, or personal project all welcome</li>\n\n</div>"
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