Senior Applied Scientist / Engineer, Training & Inference

Adobe Systems
San Jose
Workplace: OnsiteFull timeUSD 164,000 - 313,300 annuallyFunction: Research & Scientific (R&D)Education: mastersSkills: ["Collaboration","Independent ownership","Cross-team execution","Systems thinking","Problem-solving"]

Own end-to-end training-to-deployment systems for large video and multimodal generative models. You’ll drive distributed training across multi-node GPUs, optimize inference and serving for latency/throughput/cost, and harden research checkpoints into reliable production deployments. Collaborate with applied researchers, ML engineers, and infrastructure teams to align model needs with product timelines, ensuring scalability, correctness, and operational performance.

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Adobe Systems
Adobe Systems
3 days ago

Senior Applied Scientist / Engineer, Training & Inference

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Source: Company careers pageValidated by: Fursa AI
Last checked: 9 hours agoStatus: Live

Job Summary

Own end-to-end training-to-deployment systems for large video and multimodal generative models. You’ll drive distributed training across multi-node GPUs, optimize inference and serving for latency/throughput/cost, and harden research checkpoints into reliable production deployments. Collaborate with applied researchers, ML engineers, and infrastructure teams to align model needs with product timelines, ensuring scalability, correctness, and operational performance.
Location: San Jose
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Own key components of the training-to-deployment pipeline, from distributed training through inference optimization, serving, and production handoff.
  • •Implement and operate large-scale distributed training strategies (PyTorch FSDP, tensor parallelism, pipeline parallelism) for large video and multimodal models.
  • •Design and optimize inference and serving systems to meet latency, throughput, and cost targets across deployment targets.
  • •Bridge research-to-production by hardening, validating, and operationalizing trained model checkpoints at scale.
  • •Identify and address inefficiencies across the training and inference stack, including memory, communication, scheduling, and execution orchestration.

Pay and Benefits

Salary: USD 164,000 - 313,300 annually

Key Requirements

  • •Master’s or PhD in Computer Science, Electrical Engineering, AI/ML, or related field, or equivalent practical experience.
  • •Hands-on experience with large-scale distributed training using PyTorch, including FSDP, tensor parallelism, and pipeline parallelism across multi-node GPU environments.
  • •Proven experience optimizing and deploying large generative models for production, including serving infrastructure and latency/throughput and cost-aware tuning.
  • •Proficiency in Python and PyTorch with experience working in large shared codebases and contributing to production-critical ML systems.
  • •Ability to independently own end-to-end technical areas and deliver high-quality systems by driving cross-team execution from training through deployment.
Experience:Generative AIMachine learningVideo generationMultimodal
Education:Master's
Skills:CollaborationIndependent ownershipCross-team executionSystems thinkingProblem-solving
Tech Stack:PythonPyTorchFSDPTensor ParallelismPipeline ParallelismGPUTensorRTVLLM

Company Brief

Adobe Systems
Provides creative, marketing, and document management software and cloud services, including Photoshop, Illustrator, Acrobat, and the Adobe Experience Cloud, serving creative professionals, enterprises, and governments worldwide.
Industry: SaaS
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Jose, United States
Founded: 1982
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