AI Operations, Applied AI Research and Design Developer

Summary Act as the technical engine that accelerates our organization's 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.

Description 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 system's performance against the original POC results and, where applicable, human-level parity. Drive the team's 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.

Minimum Qualifications Bachelor’s or Master's 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.

Preferred Qualifications 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.

Pay & Benefits At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $129,300 and $194,700, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

EEO Statement Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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