AI Operations, Applied AI Research and Design Developer

Act as the technical engine that accelerates our organizations AI ambitions by leading the technical design and validation of new opportunities. Your work proves the viability of hyper scalable and innovative human-centered AI solutions designed to deliver accurate, timely, and compelling metadata and the highest quality video/audio for our content across Movies, TV, Live Sports, and Apple Originals.

You will own the technical validation lifecycle, from partnering with product management to translate a pitch into a feasible solution, through the development of a rapid POC to prove its viability. Throughout this journey, your work will be fueled by continuous research into the cutting-edge of AI, ensuring every "go/no-go" recommendation is backed by rigorous, data-driven evidence. Collaborate closely with the Operations Product Manager to shape proposed solutions, acting as the technical expert to assess technical feasibility while adhering to responsible AI principles. Curate and own the "golden datasets" and evals used for training, testing, and benchmarking, ensuring all performance claims are grounded in high-quality, representative data. Lead the execution of rapid proofs-of-concept (POCs) to validate technical viability by creating a testing plan and measuring performance against baselines. Own the technical handoff to engineering, translating POC findings into clear reports, demos, and hands-on guidance to ensure a successful production build-out. Lead post-launch validation by benchmarking the production systems performance against the original POC results and, where applicable, human-level parity. Drive the teams long-term technical strategy by continuously researching the cutting-edge of the AI landscape and translating those insights into actionable recommendations for the product roadmap and tooling. Build POCs and automated workflows that combine Apple-approved LLMs and multimodal models (text, image, video, audio) with tools and data to carry out multi-step tasks. This includes prompt design, structured outputs, retrieval, and failure handling. Design and run evaluations: work with domain experts to build ground-truth datasets, choose metrics that answer the business question, and run controlled experiments across models, prompts, preprocessing, and scoring Turn results into clear recommendations: technical write-ups, demos, and handoff guidance for engineering, with honest reporting of limits and failure modes.Bachelor's or Masters degree in Computer Science, AI/ML, Data Science, or a related field (completed or expected by the start date). Strong Python skills, including data manipulation and analysis (pandas, SQL), REST APIs, and structured data (JSON/XML). Hands-on experience building AI applications on LLM APIs (prompting, structured outputs, RAG, or tool-using workflows) through coursework, research, internships, or projects, plus judgment about when to use an LLM, a classical ML model, or a deterministic approach. Working knowledge of ML evaluation: precision/recall/F1, building test sets, experiment design, and validating results against baselines and benchmarks. Understanding of AI guardrails, including safety, privacy, compliance, and responsible deployment. Proficiency with LLM-assisted coding tools to design, implement, and iterate on software. Clear written and verbal communication, including technical documentation and explaining complex results to non-technical stakeholders.Internship or research experience building and benchmarking an AI system end to end, from data to model to evaluation to recommendation. Shown ability to work independently and deliver working prototypes quickly as requirements change, through a thesis, publications, or public projects. Experience translating business requirements into implementable solutions, and writing technical specifications and handoff materials for engineering. Experience with multimodal models, video/audio processing, LLM-as-judge evaluation, or open-weight model serving. Experience with agent frameworks, observability or monitoring for LLM systems, or cloud/ML infrastructure. Front-end experience (React/TypeScript) for building demos and internal tools. Familiarity with content operations, such as sports data, video metadata, content quality, or similar data-heavy operational settings.