Senior Machine Learning Architect - GenAI

Senior Machine Learning Architect

Adobe is seeking a Senior Machine Learning Architect to help define and deliver the next generation of AI-powered user experiences across Adobe Experience Cloud. This role sits at the intersection of machine learning, user experience, and large-scale UI systems, with responsibility for shaping how intelligence is delivered to users—not just which models are used.

As a senior member of the GenAI Experiences team, you will architect and evolve the ML systems that power AI-assisted experiences, including AI Assistant and related experience surfaces. This includes combining modern GenAI approaches with classical machine learning techniques to deliver responsive, reliable, and trustworthy user experiences.

This role is ideal for senior ML engineers who enjoy end‑to‑end ownership: from user‑centric data and modeling, to inference pipelines, to how intelligence is grounded in real production UIs and evaluated through user behavior.

• Build and ship ML‑driven capabilities that power AI‑assisted user experiences across Adobe Experience Cloud, with a strong emphasis on usability, trust, and proactivity.
• Design and architect ML systems that blend multiple approaches—including LLMs, classical ML/NL/IR, and heuristics—to solve complex user‑facing problems at scale.
• Work deeply with user‑centric data such as page and UI structure, semantics, and state, interaction logs, UI events, behavioural signals, and human feedback loops to inform modelling, evaluation, and iteration.
• Apply and evolve non‑GenAI techniques where appropriate, including recommendation systems, clustering, ranking, NLP pipelines, heterogeneous graph traversal, and edge‑based or on‑device models.
• Develop and evolve agentic and reasoning‑based systems that integrate retrieval, context, workflows, and decisioning in service of grounded, high‑quality user experiences.
• Partner closely with UI engineers, designers, product managers, and researchers to ensure ML capabilities are expressed through intuitive, coherent interaction patterns.
• Define and own evaluation frameworks that incorporate UX‑relevant signals such as relevance, latency, consistency, visual quality, and human feedback—not just offline accuracy metrics.
• Drive system reliability, scalability, and performance for user‑facing ML systems, including real‑time and edge inference, experimentation, and monitoring under strict latency, privacy, and compute constraints.
• Serve as a senior technical leader and mentor, helping shape ML direction and standards across experience‑focused teams.

• Bachelor’s degree with 10+ years of industry experience, or Master’s/PhD with equivalent experience, building and shipping ML systems at scale.
• Strong background in applied machine learning, spanning both modern GenAI techniques and classical ML approaches.
• Demonstrated experience architecting ML systems end‑to‑end, from data ingestion and modelling to production deployment and iteration.
• Proven success delivering AI/ML solutions that directly impact user experience, engagement, or productivity.
• Proficiency in Python and experience with ML frameworks such as PyTorch, Tensor Flow, Hugging Face, or equivalent.
• Hands‑on experience with user‑centric data pipelines, experimentation frameworks, and production evaluation.
• Experience with LLMs, retrieval‑augmented generation, prompt and context engineering and agent creation/agentic systems.
• Strong judgment in selecting the right AI/ML approach for a given problem, rather than defaulting to a single technique.
• Excellent communication and collaboration skills, with the ability to influence engineering, product, and design partners.
• A track record of technical leadership, mentorship, and shaping ML direction beyond individual projects.

• Experience with on‑device / edge ML constraints (latency, memory, privacy), and/or model optimisation for client environments (the roadmap emphasises client‑side models and “edge inference & optimisation”).
• Experience building human‑in‑the‑loop systems (annotation, feedback, calibration, explainability, interruption management).
• Experience building UI and user experience…