Senior Machine Learning Engineer ServicesMLOps

Adobe; San Jose (CA); On-site; USD 151,800 - 265,350; Full time

Job summary

Adobe in California is seeking an outstanding ML infra engineer to build and optimize the large-scale foundation model infrastructure powering Firefly. You will work on data processing, distributed training across thousands of GPUs, and GPU kernel optimization.

You'll collaborate to architect end-to-end ML pipelines, improve latency, data governance, and model deployment. Proficiency in Python, PyTorch or TensorFlow, and experience with Docker and ML ops are valued.

Qualifications

• 5+ years ML Engineering focusing on generative AI and LLMs.
• Strong Python and DL engineering with PyTorch or TensorFlow.
• Experience with distributed training, CUDA, and GPU optimization.

Responsibilities

• Build and optimize infrastructure powering foundation model training on thousands of GPUs.
• Profile GPU utilization, trace runs, and optimize latency.
• Architect end-to-end ML pipelines for scalability and robustness.
• Analyze data to guide model selection, evaluation metrics and governance.
• Engage in architecture, deployment, and optimization through the product lifecycle.

Skills

Python

PyTorch

Deep Learning

LLMs

Distributed training

GPU kernels

Education

Master's or PhD in Computer Science, Computer Engineering, or related

Tools

Docker

MLOps

AWS/Azure

CUDA

Job description

The Opportunity

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!

Responsibilities

• You will build and optimize infrastructures that power large foundation model training on thousands of GPUs.
• You will profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency.
• We will work together to architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
• You will dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
• Throughout the product lifecycle, you will engage in architecture, design, deployment, and optimizations of ML models and systems.

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.
• Familiarity with distillation, transformers, and diffusion models. Experience with generative image and video is a plus.
• Knowledge of deployment technologies such as Docker, MLOps, and ML services is valuable, and experience with cloud platforms like Azure and AWS is a plus.
• Excellent problem‑solving abilities and 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.

Pay Range

U.S. pay range for this position is $151,800 - $265,350 annually. In California: $183,300 - $265,350. In Washington: $165,600 - $239,725. The exact salary depends on location and experience. Additional roles may be eligible for long‑term incentives in the form of equity awards.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic.