Principal Scientist - Data Pipeline Engineer

We're looking for a Pr incipal 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 wor k 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.

- 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

- 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 exper tis e 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

Expected Pay Range: Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $206,300 -- $388,000 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience.

Your recruiter can share more about the specific salary range for the job location during the hiring process.

In California, the pay range for this position is $268,000 - $388,000 In Washington, the pay range for this position is $247,200 - $357,900