Adobe—Machine Learning Engineer

Job description

Description: About the Role Adobe is seeking a Machine Learning Engineer to join the Administration team in Adobe Unified Platform . This group drives the enterprise business for Adobe. This is a rare kind of role: you won't be slotting into a machine that's already running. You'll be the founding ML voice on a team of strong senior engineers who know how to ship, but who are looking to you to set the approach — to decide how models get built, trained, evaluated, and put into production. Your impact won't be measured in a single model. It'll be measured in a capability you create that the whole team builds on for years. And the problems are real. A few of the things you could own: - Revenue that responds to your models. As our growth targets climb, the game shifts from volume to conversion. You'll build propensity and conversion models that decide who we reach and how — work that shows up directly in the numbers the business cares about most. - Making sense of Gen AI at scale. Everyone wants to understand how generative AI is really being used. You'll build the models that get us there: anomaly detection, Return of Investment prediction, prompt classification — turning raw signal into insight nobody else has. - AI that actually knows the customer. We're building an administration assistant that adapts to who it's talking to. You'll train the persona and personalisation models that make it feel genuinely intelligent — real ML, not prompt-tuning around someone else's LLM. - Systems that learn from themselves. You'll design closed-loop models that take outcomes from live initiatives and feed them back to make the next round smarter. You'll have the autonomy of a founder, the backing of an established team, and the reach of Adobe — where the work you ship touches hundreds of millions of people. If you want to be the ML engineer who shaped how a team builds, this is that seat. Key Responsibilities - Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. - Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. - Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/check-pointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). - Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. - Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. - Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and production monitoring. - Collaborate cross-functionally with data science, product, and platform teams; mentor junior engineers on experimentation rigor, deployment process, and responsible AI. - Know the latest advances in ML/AI and bring relevant innovations into Adobe's products. Minimum Qualifications - Bachelor’s degree or Master’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field. - 4 - 8 years of professional experience building and deploying ML solutions at scale. - Strong programming expertise in Python, with hands-on experience in PyTorch, TensorFlow, or similar frameworks. - Deep understanding of the end-to-end ML lifecycle—from data collection to deployment and monitoring. - Strong grasp of model optimization, inference efficiency, and production system integration. 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. 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. Skills: python programming, pytorch, tensorflow, deep learning model development, model training, model optimization, distributed computing, event tokenization, temporal embeddings, self-supervised pretraining, downstream fine-tuning, gpu infrastructure, databricks, spark, feature pipeline development, model deployment, mlops, model versioning, ci/cd, automated retraining, mentoring junior engineers, cross-functional collaboration, experiment tracking