Job description

ABOUT THE ROLE 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. WHAT YOU’LL DO OPTIMIZE DATA PROCESSING PIPELINES AT SCALE 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 ARCHITECT SCALABLE DATA INFRASTRUCTURE 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 DRIVE DATA CURATION FOR MODEL TRAINING 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 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-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 About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity. Our 30,000 employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours. Let’s Adobe together At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture , focus on people, purpose and community , Adobe for All , comprehensive benefits programs , the stories we tell , the customers we serve, and how you can help us advance our mission of empowering everyone to create. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more. Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations adobe.com or call 1 408-536-3015. AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process. At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience . 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 At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC base commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP). In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award. State-Specific Notices: California : Fair Chance Ordinances Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. Colorado: Application Window Notice If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs. Massachusetts: Massachusetts Legal Notice It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Requirements

Must-haves

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
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
Fair Chance Ordinances
Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.
Application Window Notice
Massachusetts Legal Notice

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mldesign systemspythonrustgojavamachine learningai

Platform: workday
Effort: long