Senior Machine Learning Engineer, Firefly Foundry

AdobeSan Jose, CA
$151,800 - $265,350

About The Position

Our focus is developing AI technologies for text, images, and videos to boost creativity. We're seeking an outstanding ML infra engineer with deep expertise in building large scale foundation models infrastructures that support all the generative AI efforts in Firefly! This is a chance to create a huge impact in a fast-paced, startup-like environment in a great company. Join us! The position involves building infrastructures touching various components of our foundation model stack. including large scale data processing, scalable and reliable PyTorch training infrastructures, GPU optimizations with custom CUDA kernels on the latest Nvidia GPUs, and more!

Requirements

  • Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience.
  • 5+ years ML Engineering experience, specializing in generative AI like LLMs.
  • Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow, will be essential.
  • Familiarity with distillation, transformers, and diffusion models.
  • Excellent problem-solving abilities and your capacity to analyze complex issues and drive solutions with a data-driven approach.
  • Strong verbal and written communication skills and success in cross-functional team environments.

Nice To Haves

  • Experience with generative image and video is a plus.
  • Knowledge of deployment technologies such as Docker, ML Ops, and ML services is valuable.
  • Experience with cloud platforms like Azure and AWS is a plus.

Responsibilities

  • Build and optimize infrastructures that power large foundation model training on thousands of GPUs
  • Profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency.
  • Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
  • Dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
  • Engage in architecture, design, deployment, and optimizations of ML models and systems throughout the product lifecycle.

Benefits

  • Comprehensive benefits programs
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