below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$177,250–$241,906.
Based on 240 similar postings.
Employer
About Adobe
Adobe Inc. is a global software company known for creative and multimedia software products including Photoshop, Illustrator, Acrobat, and its cloud-based Creative Cloud and Document Cloud suites. Industry: Creative & Digital Experience Software
Adobe currently has
282 open roles
on FindRole.
Listed pay typically runs
$148,100–$282,100
across 76 roles with salary data.
JOB TITLE: Senior Applied Scientist / Engineer, Training & Inference
The Adobe Applied Science & Machine Learning team is seeking a Senior Applied Scientist / Engineer, Training & Inference to bridge the gap between research and production for next-generation video and multimodal generation models. You will serve as the technical owner for the training-to-deployment pipeline, moving large generative models from research clusters to reliable, cost-efficient production environments. Key responsibilities include implementing distributed training strategies like PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism across multi-node GPU environments. You will also design and optimize inference and serving systems, focusing on latency, throughput, and GPU efficiency. The role requires proficiency in Python and PyTorch to harden, validate, and operationalize models, specifically addressing technical challenges in video and image generation, including diffusion models and flow matching.
Own the end-to-end training-to-deployment pipeline for video and multimodal generative models.
Implement distributed training strategies using PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism.
Design and optimize inference and serving systems for low latency, high throughput, and cost-efficiency.
Bridge the gap between research checkpoints and production by hardening and operationalizing models at scale.
Identify and resolve inefficiencies in memory, communication, scheduling, and execution orchestration.
Profile and optimize performance for both training and inference workloads on multi-node GPU environments.
What we're looking for
Master's or PhD in Computer Science, Electrical Engineering, AI/ML, or a related field, or equivalent practical experience.
Hands-on experience with large-scale distributed training using PyTorch (FSDP, Tensor Parallelism, Pipeline Parallelism) across multi-node GPU environments.
Proven experience optimizing and deploying large generative models for production, including serving infrastructure and latency/throughput tuning.
Proficiency in Python and PyTorch with experience contributing to production-critical ML systems and large shared codebases.
Demonstrated ability to independently own end-to-end technical areas and navigate the engineering challenges of reliability and operational scale.
Experience training and deploying video, image, or multimodal generative models (preferred).
Familiarity with inference serving frameworks such as TensorRT, vLLM, or equivalent (preferred).
Experience with performance profiling and optimization for both training and inference workloads (preferred).
Python
PyTorch
Generative AI
Diffusion Models
VLM
Distributed Training
Multi-modality
Deep Learning
Data Curation
Captioning Pipelines
Image-to-Image
Mid-training
Pre-training
Post-training
PyTorch
FSDP
Tensor Parallelism
Pipeline Parallelism
Distributed Training
Inference
Model Architecture
Generative AI
Diffusion
Flow Matching
Computer Vision
Data Pipelines
Checkpointing
Fault Tolerance
Foundation Models