ML - Data Scientist

Do you want to decide whether a generative imaging feature is good enough to ship? Our team evaluates generative image and video features. These features reach a very large number of people on Apple devices. You will build the evaluation systems, human-annotation programs, and automated metrics that turn "this looks better" into measurable, trustworthy quality signals. Those signals guide model development and release decisions.

Description

You'll own quality evaluation end to end — from evaluation set design through annotation, scoring, analysis, and the recommendations that shape what engineering and product build next. Your findings determine which models ship.

What you'll do Build the measurement foundation. Design and maintain golden sets and evaluation sets, analyzing data distributions so results stay representative and known defects stay covered.

Run human evaluation at quality. Write and iterate annotation guidelines and label schemas, build or adapt lightweight labeling tools for image and video review, monitor annotator quality, and resolve disagreements and guideline ambiguity.

Automate the repeatable parts. Develop automated metrics and LLM-as-judge systems, calibrate them against human labels, and track where they diverge.

Make the results decision-ready. Report model-vs-model comparisons with confidence intervals, significance testing, and breakdowns by hotspot, content type, and failure mode — then turn them into root-cause findings and prioritized recommendations.

Set the bar with your partners. Work with ML, engineering, and design to define quality standards, and communicate results clearly to technical and non-technical audiences alike.

Minimum Qualifications

MS and a minimum of 5 years relevant industry experience 5+ years of experience in data science, machine learning evaluation, algorithm analysis, or related technical disciplines. Experience designing and analyzing evaluations and experiments, especially for ML or generative model quality. This includes sampling design, significance testing, and confidence intervals. Strong Python skills and comfort with SQL, with the ability to build and maintain evaluation pipelines and small tools. Familiarity with AI-assisted analysis workflows, foundation models, agentic systems, or intelligent automation approaches for technical problem solving.

Preferred Qualifications

Experience evaluating 3D Gaussian splats or diffusion models, or assessing perceptual quality. Experience building LLM or VLM judges and validating them against human ratings. Experience building web-based labeling or review tools. Ability to run several work-streams in parallel and prioritize them. Proven cross-functional collaboration and the ability to learn new tools quickly. Strong written and verbal communication, including explaining statistical tradeoffs to engineers and product partners.