Senior Machine Learning Engineer

Our Company

Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging artists to global brands-everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.

We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

About the Role

We are looking for a Senior Machine Learning Engineer to join our ML Data Platform team, responsible for building the foundational infrastructure that powers large-scale multimodal AI training and inference at Adobe. This role is ideal for someone who thrives at the intersection of distributed systems, ML infrastructure, and data engineering-building the pipelines, feature stores, and batch inference systems that enable ML teams to operate at scale. You'll work on high-impact projects involving petabyte-scale data processing, enrichment pipelines, and production-grade ML infrastructure that supports generative AI across Adobe's product suite.

If you're excited about architecting systems that process multimodal data at massive scale, optimizing distributed inference workloads, and shaping the future of ML data infrastructure, we'd love to hear from you.

What You'll Do

- Design and build distributed inference applications that process large-scale multimodal data (images, video, documents, text) across thousands of GPUs.

- Architect and implement feature stores and data enrichment pipelines that serve as the backbone for Foundational model training.

- Build and maintain backend platform services that power ML workflows, including job orchestration, queue management, and resource allocation.

- Develop and optimize batch inference systems using frameworks like Apache Ray, Spark ML, or similar distributed computing tools.

- Implement and manage semantic search capabilities and vector database infrastructure (e.g., OpenSearch, LanceDB, Pinecone) for embedding-based retrieval.

- Collaborate with ML research teams to translate model requirements into scalable, production-ready infrastructure.

- Create reusable frameworks, templates, and documentation to accelerate ML platform adoption across teams.

- Collaborate with product, legal, and policy teams to ensure regulatory compliance.

- Mentor engineers and contribute to a culture of technical excellence and architectural rigor.

What You Need to Succeed

- 7+ years of professional experience designing, building, and operating large-scale distributed systems and production-grade ML infrastructure.

- Proven expertise building distributed inference applications on large-scale multimodal data.

- Strong expertise in cloud infrastructure and distributed computing.

- Deep hands-on experience with ML frameworks such as PyTorch or TensorFlow in production environments.

- Strong understanding of batch inference architectures and frameworks (e.g, Apache Ray, Spark ML, Dask, or equivalent).

- Solid MLOps experience including CI/CD for ML, model registries, and deployment automation.

- Proficiency in Python and strong software engineering fundamentals (system design, data structures, algorithms).

- Familiarity with cloud platforms (AWS or Azure) and data platforms (Databricks, Spark).

- Master's degree (MS or Ph.D.) in Computer Science, Machine Learning, or a related field preferred.

- Excellent communication skills and ability to collaborate across ML research and engineering teams.

Join us and help drive the next decade of growth at Adobe! Your innovative ideas and dedication to excellence will make a difference.

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.

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California:

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Colorado:

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Massachusetts:

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