About the job
We’re looking for a Principal ML Engineer to architect and scale the multimodal data processing pipelines and infrastructure behind Adobe Firefly’s multimodal foundation models (image, video, audio). In this role, you’ll sit 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 directly determine how fast and how well Adobe models can learn directly impacted by the throughput and reliability of our data pipelines, and the quality of data that reaches training. This is a senior individual contributor role with broad technical influence across data, infrastructure, and modeling teams.
Responsibilities
Architect and optimize large-scale distributed pipelines that process billions of images, video, and audio assets through ML workflows into training-ready data
Scale up inference throughput across the pipeline (batching, parallelism, hardware utilization) to turn raw collected data into training data faster and more cheaply
Identify and eliminate bottlenecks across ingestion, processing, and delivery, from storage and I/O to compute scheduling
Design systems that reliably store, index, and serve billions of data points, each requiring substantial processing spanning large-scale databases, distributed storage, and high-throughput compute
Apply deep expertise in distributed systems and frameworks such as Ray (or equivalent) to orchestrate large-scale, GPU/CPU-heavy data workloads
Own architecture decisions including database and storage choices, job scheduling, GPU cluster utilization that let the platform scale alongside data and model growth
Bring a strong ML background, especially inference optimization for VLMs and LLMs and data curation for training
Partner closely with modeling teams to understand what data improves training outcomes, and translate that into pipeline and curation requirements
Operate as a hands-on technical leader who bridges data engineering and applied ML
Qualifications
Minimum
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-on expertise in distributed systems and frameworks such as Ray, Spark, or equivalent large-scale data processing frameworks
Proficiency in 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 scale such as data lakes, indexing, and retrieval across billions of data points
Strong ML background, particularly expertise in optimizing GPU 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
Comfort operating across 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
Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Machine Learning, or a related field
Preferred
No preferred qualifications listed.