Senior Applied Scientist: Training-to-Deployment Lead

Adobe; San Jose (CA); On-site; USD 216,000 - 313,000; Full time

Job summary

Adobe Applied Science & Machine Learning (ASML) is seeking a Senior Applied Scientist/Engineer to own the training-to-deployment pipeline for video and multimodal models. You will bridge research and production, driving distributed training at scale, inference optimization, and reliable deployment.

You will collaborate with researchers, ML engineers, and infrastructure teams to deliver cost-efficient, high-performance systems.

Qualifications

• Master's or PhD in CS, EE, AI/ML, or equivalent practical experience.
• Hands-on distributed training with PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism.
• Experience optimizing and deploying large generative models for production.
• Proficiency in Python and PyTorch; able to work in large shared codebases.
• Ability to own end-to-end technical areas and drive cross-team delivery.

Responsibilities

• Training & Inference Ownership: Own training-to-deployment pipeline components.
• Large-Scale Distributed Training: Implement distributed training strategies across multi-node GPUs.
• Inference & Serving: Design and optimize latency, throughput, and cost.
• Research-to-Production Bridge: Harden, validate, and operationalize models at scale.
• Performance & Cost-Aware Engineering: Address memory, communication, scheduling, and GPU efficiency.
• Collaboration with Research & Engineering Teams: Align systems with product delivery timelines.

Skills

Distributed Training

Inference & Deployment

Python

PyTorch

Systems Engineering

Education

Master's or PhD in CS/EE/AI/ML

Tools

PyTorch FSDP

Tensor Parallelism

Pipeline Parallelism

Job description

Adobe Applied Science & Machine Learning (ASML) is seeking a Senior Applied Scientist/Engineer to own the training-to-deployment pipeline for video and multimodal models. You will bridge research and production, driving distributed training at scale, inference optimization, and reliable deployment.

You will collaborate with researchers, ML engineers, and infrastructure teams to deliver cost-efficient, high-performance systems.