On-Device Computer Vision & Vision-Language Engineer

Apple

Cupertino (CA)

In loco

USD 150.000 - 278.000

Tempo pieno

6 giorni fa
Candidati tra i primi

Una candidatura completa in un minuto

Curriculum e lettera di presentazione personalizzati, pronti da inviare.

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Vantaggi offerti da questo lavoro

Employee stock programs
Stock awards
Employee stock purchase plan
Medical and dental coverage
Retirement benefits
Product discounts
Relocation

Descrizione del lavoro

Apple in Cupertino, CA is seeking a researcher to build and improve computer vision and vision-language models that enable on-device understanding of objects. You will design, train, and iterate models tailored to device constraints.

You will work on end-to-end projects from data collection to evaluation, emphasizing robustness across real-world scenarios, performance under memory and latency limits, and integration with on-device software.

Competenze

  • Ph.D. or Master’s in CS/ML/CV or related field, or equivalent industry experience.
  • Strong foundations in computer vision and deep learning.
  • Experience delivering end-to-end projects with data, training, and evaluation on real-world data.
  • Proficiency in Python and PyTorch.

Mansioni

  • Design, train, and improve CV and vision-language models for object understanding.
  • Increase robustness across devices by addressing real-world failure modes.
  • Define evaluations aligned to product quality to guide model decisions.
  • Develop on-device models respecting memory, latency, and power constraints.

Conoscenze

Computer vision
Deep learning
Generative coding tools
Experimentation

Formazione

Ph.D. or Master’s in CS/ML/CV or related field

Strumenti

Python
PyTorch

Descrizione del lavoro

Apple in Cupertino, CA is seeking a researcher to build and improve computer vision and vision-language models that enable on-device understanding of objects. You will design, train, and iterate models tailored to device constraints.

You will work on end-to-end projects from data collection to evaluation, emphasizing robustness across real-world scenarios, performance under memory and latency limits, and integration with on-device software.

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