Principal Scientist Data Pipeline Engineer

ABOUT THE ROLE

Werelookingfor a PrincipalMLEngineer to architect and scale themultimodaldataprocessingpipelines and infrastructure behind Adobe Fireflys multimodal foundation models (image video audio). In this roleyoullsit at the intersection of data engineering and applied ML building distributed GPU-accelerated systems that turn billions of raw assets into training-ready data at scale.

Your work will directlydeterminehow fast and how wellAdobemodels can learndirectlyimpactedbythe throughput and reliability of our data pipelines and thequality of data that reaches training. This is a senior individual contributor role with broad technical influence across data infrastructure and modeling teams.

WHAT YOULL DO

OPTIMIZE DATA PROCESSING PIPELINES AT SCALE

• Architect andoptimizelarge-scaledistributed pipelines that process billions of images video and audio assetsthrough ML workflowsinto training-ready data

• Scale up inference throughput across the pipeline(batching parallelism hardwareutilization)to turn raw collected data into training data faster and more cheaply

• Identifyandeliminatebottlenecks across ingestion processing and delivery from storage and I/O to compute scheduling

ARCHITECTSCALABLE DATA INFRASTRUCTURE

• Design systems that reliably store index and serve billions of data points each requiring substantial processingspanning large-scale databases distributed storage and high-throughput compute

• Apply deepexpertisein distributed systems and frameworks such as Ray (or equivalent) to orchestrate large-scale GPU/CPU-heavy data workloads

• Own architecture decisionsincludingdatabase and storage choices job scheduling GPU clusterutilizationthat let the platform scale alongside data and model growth

DRIVE DATA CURATION FOR MODEL TRAINING

• Bring a strong ML backgroundespecially inference optimization for VLMs and LLMsanddatacurationfor training

• Partner closely with modeling teams to understand what dataimprovestraining outcomes and translate that into pipeline and curation requirements

• Operate as a hands-on technical leader who bridges data engineering and applied ML

WHAT YOU NEED TO SUCCEED

• 10 years of experience in data engineering ML infrastructure or distributed systems including work at large scale (billions of records or assets)

• Strong software engineering background with hands-onexpertisein distributed systems and frameworks such as Ray Spark or equivalent large-scaledataprocessing frameworks

• Proficiencyin Python plus strong experience in a systems-level language (C Rust Go or Java) with strong debugging skills across distributed and ML-centric runtime environments.

• Deep knowledge of databases and storage systems at scalesuch asdata lakes indexing and retrieval across billions of data points

• Strong ML background particularlyexpertiseinoptimizingGPU inference pipelines for VLMs LLMs or other large models (batching quantization serving throughput/latency tradeoffs)

• Experience with data curation for model training:understanding what makes data valuable for training generative or multimodal models not just how to move it efficiently

• Comfortoperatingacross the full stack from low-level systems and GPU optimization to higher-level data strategy and curation decisions

• Ability to communicate clearly and partner effectively across data infrastructure and modeling teams

• Bachelors Masters or Ph.D. in Computer Science Engineering Machine Learning or a related field