# Principal ML Engineer

**Company**: Adobe
**Location**: San Jose
**Experience**: senior
**Job type**: full-time
**Salary**: $206,300 - $388,000 annually
**Category**: Engineering
**Industry**: Technology

**Apply**: https://adobe.wd5.myworkdayjobs.com/en-US/external_experienced/job/San-Jose/Principal-Scientist---Data-Pipeline-Engineer_R170588?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_a75486f6-e61

## Description

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.

**Responsibilities**

- Own 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

**Requirements**

- 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

## Skills

### Required
- Python
- C++
- Rust
- Go
- Java
- Ray
- Spark
- ML systems
- ML infrastructure
- applied ML engineering

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Source: [Apply at adobe.wd5.myworkdayjobs.com](https://adobe.wd5.myworkdayjobs.com/en-US/external_experienced/job/San-Jose/Principal-Scientist---Data-Pipeline-Engineer_R170588?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)