Principal Machine Learning Engineer - Aitopics

Adobe

Adobe is changing the world through digital experiences. We help our customers create, deliver and optimize content and applications.

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!

The Opportunity
The Adobe Document Cloud's AI & Innovation Team is looking for an experienced Machine Learning Engineer to help with a variety of Evaluations related to LLMs. The Adobe Document Cloud is responsible for technologies for building, manipulating and consuming PDF files across a range of services, mobile apps and desktop products. The flagship PDF application within DC is Adobe Acrobat, which is installed on over a billion devices and opens billions of PDFs every year. Our offerings extend beyond Acrobat to Adobe Sign, Document Cloud Platform, and Adobe Scan.

The team’s mission is to perform scientific evaluation through metrics, experiments and inferences to build new experiences with documents, across our entire suite of offerings. Our most recent public features are Liquid Mode, allowing a flawless reflow experience for PDFs on phones and tablets; PDF Extract API for unlocking the structure and content elements of any PDF; Table Decomposition feature in Liquid Mode; and now new generative AI features. All products are driven by AI. Our solutions are deployed on mobile devices, in the cloud, and on desktop.

As a Machine Learning Engineer, you will get to democratize (generative) AI for all and empower a team of accomplished scientists and engineers who are dedicated to building the future of AI and documents and delivering delightful experiences.

What you'll do
We are looking for a Machine Learning Engineer to design, lead and facilitate decisions on our features being built through scientific evaluations which includes defining metrics, hypotheses testing, experimental design, causal inference, and A/B tests. If you have a passion for shipping groundbreaking data-driven customer-facing features, learning new technologies every day, and working on high visibility projects with ample room for creativity, this might be a good fit for you. On the team you will join a diverse, lively group of engineers and scientists.

The work is dynamic, fast-paced, creative, collaborative and data-driven.
What we are looking for:

• 8+ years of industry work experience. Good sense of product with a focus on shipping user-facing data-driven features.
• Experience with Gen AI Evaluations, statistical methodologies, like hypotheses testing, time series analysis, mathematical modeling, linear and non-linear regression, stochastic modeling.
• Experience with experimentation, A/B tests, causal inference.
• Advanced analytical abilities, defining metrics, analyzing data on user interaction with our features to support data-driven decisions.
• Design, architect and build data insight dashboards and visualizations.
• Knowledge of ML Ops. Familiarity with ML services is a plus.
• Some experience with system design, ML model assessment, building data pipelines, automation architecture, system deployment.
• Provide thought leadership to the data-driven community by actively mentoring teammates in standard methodologies to self-serve using the most efficient code and techniques.
• Python and Python based ML and Data Science frameworks.
• Excellent coding, analysis, and problem-solving skills.
• Strong communication and collaboration skills.
• Master's or PhD in statistics, econometrics, operations research, applied physics, engineering, or a related quantitative field.

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