Drimva

Apple

AIML - Machine Learning Engineer, Visual Intelligence Technology

$175K–$325K

Santa Clara, California +1 · Full-time · Posted Jan 28, 2026

Location
Santa Clara, California
Seattle, Washington
Experience
5+ years required; preferred qualification asks for 3+ years of LLM-related experience.
Education
MS or PhD in a listed or related field; no equivalent-experience alternative stated.

About the job

Build and advance machine-learning systems for visual intelligence features, including large-scale models that understand visual scenes and domain-specific LLMs for Apple products.

What you'll do

  • Explore models that understand and reason about complex visual scenes; design large-scale training pipelines and run experiments.
  • Develop, fine-tune, and evaluate domain-specific LLMs with researchers and data scientists.
  • Conduct applied research to transfer generative AI advances into production-ready technologies.

What they ask for

  • MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science, or a related field.
  • 5+ years of experience in machine learning, deep learning, information retrieval, natural language processing, or data mining.
  • Experience as a tech lead successfully leading a team to ship software to users.
  • Excellent knowledge and practical skills in designing ML platforms, architectures, or algorithms; strong interpersonal skills and ability to work independently and in a team.

Nice to have

  • 3+ years of experience in prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs.
  • Experience with Keras, Tensorflow, or PyTorch.

Pay

$175,000 and $308,500 (USD, base pay range); $184,700 and $324,800 (USD, base pay range)

Tools

Visual Search, LLM, Keras, Tensorflow, PyTorch

AI focus

Develop and evaluate large-scale visual scene understanding models and domain-specific LLMs; apply generative AI research to production.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)