AI Operations & Applied AI Research Developer

Quick Facts

• Role: AI Operations & Applied AI Research Developer

Description

Act as the technical engine behind the organization’s AI initiatives by owning technical design and validation of new opportunities. Partner with Product to translate pitches into feasible solutions, build rapid POCs, and use cutting-edge research to support rigorous, data-driven go/no-go decisions.

Responsibilities

• Collaborate with the Operations Product Manager to assess technical feasibility while following responsible AI principles

• Curate and own golden datasets and evaluation sets used for training, testing, and benchmarking

• Build and run rapid proofs-of-concept (POCs) using a defined testing plan and baseline comparisons

• Produce technical handoffs to engineering via reports, demos, and hands-on guidance

• Perform post-launch validation by benchmarking production performance vs. original POC results and, when applicable, human-level parity

• Drive long-term technical strategy through continuous AI research and actionable recommendations for the product roadmap and tooling

• Build POCs and automated workflows using Apple-approved LLMs and multimodal models (text, image, video, audio), including prompt design, structured outputs, retrieval, and failure handling

• Design and run evaluations: build ground-truth datasets, select metrics aligned to the business question, and run controlled experiments

• Turn evaluation results into clear technical write-ups, demos, and honest recommendations including limits and failure modes

Requirements

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field

• Strong Python skills for data manipulation and analysis (pandas, SQL), plus REST APIs and structured data (JSON/XML)

• Hands-on experience building AI applications on LLM APIs (prompting, structured outputs, RAG, or tool-using workflows) via projects, research, internships, or coursework

• Judgment on when to use an LLM vs. a classical ML model vs. a deterministic approach

• Working knowledge of ML evaluation (precision/recall/F1, test set creation, experiment design, baseline/benchmark validation)

• Understanding of AI guardrails: safety, privacy, compliance, responsible deployment

• Proficiency with LLM-assisted coding tools to build, implement, and iterate on software

• Clear written and verbal communication for technical documentation and stakeholder explanations

Benefits

• Own the full technical validation lifecycle (data/evals → POC → engineering handoff → post-launch benchmarking)

• Use rigorous, data-driven evaluation to make go/no-go decisions

• Contribute to human-centered AI solutions for Movies, TV, Live Sports, and Apple Originals