Autograder & GenAI Evaluation Lead

Apple, Inc.

Cupertino (CA)

In loco

USD 206.000 - 356.000

Tempo pieno

37 ore fa
Candidati tra i primi

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Vantaggi offerti da questo lavoro

Employee stock programs
Relocation assistance
Tuition reimbursement

Descrizione del lavoro

Apple Cupertino seeks a hands‑on AIML Manager to lead a team of Applied AI Scientists building state‑of‑the‑art autograder systems for evaluating GenAI products at scale. You will set the technical direction, grow the team, and partner across Apple to make automated evaluation a foundation for product quality decisions.

The role demands 5+ years in ML, experience leading 3+ engineers/scientists, and a deep grasp of GenAI evaluation methods.

Competenze

  • MS/PhD in CS/ML/AI or related field.
  • Experience building LLM/VLM evaluators or autograders.
  • Proven leadership of teams of 3+ ML engineers/AI scientists.
  • Deep understanding of GenAI models and evaluation methodologies.

Mansioni

  • Lead and grow a high‑performing team of applied AI scientists and ML engineers, including hiring and talent development.
  • Set the technical vision and roadmap for autograder development, evaluation methodology, and toolings.
  • Deliver reliable, scalable LLM/VLM‑based evaluation systems for product‑quality decisions.
  • Navigate cross‑functional dependencies and align across organizations.
  • Standardize and automate end‑to‑end evaluation workflow to scale beyond bespoke autograder development.

Conoscenze

Team leadership
Applied AI/ML
Autograder systems
Rubric design

Formazione

MS/PhD in Computer Science or ML/AI

Strumenti

LLM/VLM evaluators
Autograder development

Descrizione del lavoro

Apple Cupertino seeks a hands‑on AIML Manager to lead a team of Applied AI Scientists building state‑of‑the‑art autograder systems for evaluating GenAI products at scale. You will set the technical direction, grow the team, and partner across Apple to make automated evaluation a foundation for product quality decisions.

The role demands 5+ years in ML, experience leading 3+ engineers/scientists, and a deep grasp of GenAI evaluation methods.

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