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 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 and own the golden datasets and evals used for training testing and benchmarking ensuring all performance claims are grounded in high-quality representative the execution of rapid proofs-of-concept (POCs) to validate technical viability by creating a testing plan and measuring performance against the technical handoff to engineering translating POC findings into clear reports demos and hands-on guidance to ensure a successful production post-launch validation by benchmarking the production systems performance against the original POC results and where applicable human-level 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 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 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 scoringnTurn results into clear recommendations: technical write-ups demos and handoff guidance for engineering with honest reporting of limits and failure modes.

Bachelors or Masters degree in Computer Science AI/ML Data Science or a related field (completed or expected by the start date).nStrong Python skills including data manipulation and analysis (pandas SQL) REST APIs and structured data (JSON/XML).nHands-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 knowledge of ML evaluation: precision/recall/F1 building test sets experiment design and validating results against baselines and of AI guardrails including safety privacy compliance and responsible with LLM-assisted coding tools to design implement and iterate on 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 ability to work independently and deliver working prototypes quickly as requirements change through a thesis publications or public translating business requirements into implementable solutions and writing technical specifications and handoff materials for with multimodal models video/audio processing LLM-as-judge evaluation or open-weight model with agent frameworks observability or monitoring for LLM systems or cloud/ML -end experience (React/TypeScript) for building demos and internal with content operations such as sports data video metadata content quality or similar data-heavy operational settings.