Principal Scientist - Data Pipeline Engineer

ABOUT

THE ROLE We're looking for a Principal ML Engineer to drive down cost per asset and drive up throughput across the ML pipelines behind Adobe's multimodal foundation models (image, video, audio). You'll own the end-to-end economics of turning billions of raw assets into training-ready data, making it faster and cheaper without sacrificing data quality. This is a senior individual contributor role with broad influence across modeling and infrastructure teams.

WHAT YOU'LL DOOwn cost per asset across the full pipeline, including compute (GPU and CPU), storage, data access and movement, and reprocessing, and identify the biggest levers to reduce it Optimize pipeline DAGs to cut cost, such as running cheap filters first so expensive model stages only process the assets that matter and reduce waste from unnecessary duplications

Improve throughput of pipelines running specialized and general-purpose models (VLMs, LLMs, captioners, embedding and quality models) through batching, parallelism, quantization, and serving optimizations

Distill larger models into smaller, cheaper ones that preserve labeling and filtering quality

Decide where specialized models, general models, or cascades give the best cost/quality tradeoff

Build evaluation frameworks to ensure efficiency gains don't degrade training data quality

Develop a deep understanding of what makes data valuable for training LLMs, VLMs, and diffusion models, and encode it into filtering, scoring, and selection

Partner with modeling teams to connect pipeline changes to training outcomes

Work with data platform teams on orchestration (e.g., Ray), scheduling, and storage so ML workloads run cost-efficiently on shared infrastructure WHAT YOU NEED TO SUCCEED
10+ years in ML systems, ML infrastructure, or applied ML engineering, including work at billions-of-assets scale

Track record of cutting the cost of large-scale ML data pipelines across compute, storage, and data access (e.g., early filtering, deduplication, model cascades)
Deep expertise in optimizing inference for VLMs, LLMs, or other large models, including batching, quantization, serving, and distillation

Experience with data curation and data quality for training generative or multimodal models

Strong Python, plus a systems-level language (C++, Rust, Go, or Java), and experience with distributed frameworks such as Ray or Spark Clear communication across modeling, infrastructure, and data teams

Bachelor's, Master's, or Ph.D. in CS, Engineering, ML, or a related field

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