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All Tech JobsThe whole board, newest first.Roles That Fit MeAnswer a few questions, see your matches.Early WindowFound before the big boards.Direct ApplyStraight to the manager, past the ATS.$164,000 - 313,300/ year
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Adobe Applied Science & Machine Learning (ASML) is seeking a Senior Applied Scientist / Engineer, Training & Inference to play a critical role in closing the gap between research and production for Adobe's next-generation video and image foundation models.
In this role, you will serve as a technical owner for the training-to-deployment pipeline for our video and multimodal generation models. Rather than focusing solely on model research or systems infrastructure in isolation, you will bridge both — bringing the hands-on training expertise and the inference and deployment depth needed to take large generative models from the research cluster to reliable, performant, and cost-efficient production.
This role is ideal for those who excels at the full arc of model development — distributed training at scale, inference optimization, and the practical engineering required to deploy and operate models reliably in production.
Job Responsibilities
Training & Inference Ownership. Own key components of the training-to-deployment pipeline — from distributed training execution through inference optimization, serving, and production handoff — ensuring models are delivered reliably, performantly, and cost-efficiently.
Large-Scale Distributed Training. Implement and operate distributed training strategies including PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism across multi-node GPU environments, ensuring correctness, stability, and scalability for large video and multimodal models.
Inference & Serving. Design and optimize inference and serving systems for large generative models, with a focus on latency, throughput, and cost across deployment targets.
Research-to-Production Bridge. Reduce the gap between trained model checkpoints and reliable production deployments — owning the practical work of hardening, validating, and operationalizing models at scale.
Performance & Cost-Aware Engineering. Identify and address inefficiencies across the training and inference stack — memory, communication, scheduling, and execution orchestration — with a clear focus on GPU efficiency and cost targets.
Collaboration with Research & Engineering Teams. Partner closely with applied researchers, ML engineers, and infrastructure teams to align training and inference systems with model architecture needs and product delivery timelines.
From the office
Adobe
Offices in San Jose, United States, United States
Also hiring in San Francisco, United States, Seattle, United States, New York, United States and 23 more places
15 of their 304 open roles are remote
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