Machine Learning Engineer 50

Adobe•San Jose, CA

15h

About The Position

Adobe Journey Optimizer B2B is redefining how enterprises engage buying groups through AI-powered customer journey orchestration. We're building intelligent systems that understand complex B2B buyer behavior, predict intent signals across accounts, and deliver hyper-personalized experiences at every touchpoint—from first awareness through closed revenue. We are looking for a Machine Learning Engineer & Architect to join our AI and Agents team, define and own the ML architecture vision for our B2B journey orchestration platform. In this role, you'll shape how thousands of B2B enterprises leverage AI to transform pipeline generation, accelerate deal velocity, and drive measurable revenue impact. Your architecture decisions will power billions of personalized interactions annually, directly influencing how marketing and sales teams identify, engage, and convert buying committees. What you'll do with us You'll train and finetune ML models that solve business use cases and handle data at scale. We'll work together to architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust. You'll dive deep into data to recommend the right models, evaluation metrics, and governance approaches. Provide hands-on technical leadership, guiding engineers through architecture, building, implementation, and established guidelines. Work across organizational boundaries to align priorities and drive projects forward. Throughout the product lifecycle, you'll engage in architecture, design, deployment, and production operations of ML models and systems What will help you thrive: 10+ years of proven experience in machine learning with successful delivery of ML projects, and 3+ years of hands-on experience working with generative AI technologies such as LLMs, evaluations, fine-tuning, and more. Your strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow/JAX, will be essential. Experience with post-training techniques such as fine-tuning, alignment or distillation. Knowledge of deployment tools like Docker, ML Ops, and ML services is valuable, and experience with cloud platforms like Azure and AWS is a plus. Your strong verbal and written communication skills and success in multi-functional team environments will help us all succeed together.

Requirements

10+ years of proven experience in machine learning with successful delivery of ML projects, and 3+ years of hands-on experience working with generative AI technologies such as LLMs, evaluations, fine-tuning, and more.
Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow/JAX, will be essential.
Experience with post-training techniques such as fine-tuning, alignment or distillation.
Knowledge of deployment tools like Docker, ML Ops, and ML services is valuable, and experience with cloud platforms like Azure and AWS is a plus.
Strong verbal and written communication skills and success in multi-functional team environments will help us all succeed together.

Nice To Haves

Hands-on experience with retrieval-augmented generation (RAG), semantic embeddings, agentic AI workflows, and ML inference systems for personalization or recommendation use cases
Published research, contributed to open-source ML projects, or hold patents in AI/ML domains
Experience with Adobe Experience Platform, Marketo Engage, or Journey Optimizer—understanding how ML integrates with enterprise customer data infrastructure

Responsibilities

Train and finetune ML models that solve business use cases and handle data at scale.
Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
Dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
Provide hands-on technical leadership, guiding engineers through architecture, building, implementation, and established guidelines.
Work across organizational boundaries to align priorities and drive projects forward.
Engage in architecture, design, deployment, and production operations of ML models and systems