Staff Machine Learning Training Framework Engineer, GenAI

Adobe Applied Science & Machine Learning (ASML) is seeking a Staff Machine Learning Training Framework Engineer to play a critical role in building and scaling the core training systems behind Adobe's generative AI foundation models.

In this role, you will serve as a senior technical owner for key components of our training framework, translating research needs into reliable, scalable, and highperformance training infrastructure. Rather than focusing on a single model, your work will enable multiple multimodal and video foundation models by strengthening the shared systems used to train them.

You will operate at the intersection of applied research and largescale systems execution, ensuring that training workflows are robust, reproducible, and performant across large GPU clusters. This role is ideal for a senior engineer who thrives on deep technical ownership, complex execution, and close collaboration with research teams.

• Training Framework Ownership: Own the design and implementation of major components of the training framework, including abstractions for model configuration, optimizer and scheduler integration, checkpointing, and experiment management.
• LargeScale Training Execution: Implement and support distributed training strategies such as PyTorch FSDP, Tensor Parallelism, and Pipeline Parallelism, ensuring correctness, stability, and scalability across multinode GPU environments.
• Reliability & Fault Tolerance: Improve the resilience of longrunning training jobs by strengthening restartability, state management, and failure handling mechanisms.
• PerformanceAware Framework Design: Identify frameworklevel inefficiencies and reduce overhead related to memory usage, communication, or execution orchestration in large training runs.
• Research Enablement: Partner directly with applied researchers to support new model architectures and training requirements, ensuring the framework adapts quickly to evolving research needs.
• Training Pipeline Integration: Collaborate with infrastructure and platform teams to integrate the training framework with scheduling, storage, monitoring, and logging systems used in productionscale environments.

• Education: Master's or PhD degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
• Strong Systems Engineering Skills: Proficiency in Python and C++, with experience contributing to large, shared codebases that support multiple users or teams.
• Proven ML Training Experience: Handson experience training models using PyTorch (or JAX), including multiGPU and multinode distributed training setups.
• Distributed Systems Understanding: Solid understanding of synchronization, state management, fault tolerance, and performance tradeoffs in distributed systems.
• SeniorLevel Execution: Demonstrated ability to independently own complex technical problems, drive solutions to completion, and deliver highquality systems relied upon by others.

• Experience supporting largescale foundation model training or longrunning multinode training jobs.
• Familiarity with ML training infrastructure such as DeepSpeed, Accelerate, or internal training platforms.
• Experience working closely with applied research teams on rapidly evolving model requirements.
• Exposure to profiling, debugging, and optimizing training performance at scale.

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe's industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We're on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.

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At Adobe, we empower employees to innovate with AI - and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it's restricted during live interviews. See how we think about AI in the hiring experience.

Expected Pay Range:

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-Specific Notices:

California:

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Colorado:

Application Window Notice

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

Massachusetts:

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