Principal Engineer AI/ML

Adobe•San Jose, CA

1d•$206,400 - $379,100

About The Position

The Opportunity We are seeking a highly experienced and innovative Principal Engineer to join the GenStudio Engineering team. This role is ideal for a technical leader who thrives in a fast-paced environment and is passionate about building scalable, high-impact software solutions. You will drive architectural decisions, mentor engineers, and influence the technical direction of GenStudio. What You’ll Do Shape the technical vision and roadmap for GenAI initiatives that extract structured insights from brand knowledge and transform them into verified, on-brand content at scale. Leverage foundational principles of NLP, Linguistics, and Computer Vision to architect multimodal systems that push the frontier of grounded content generation across text, image, and video. Drive innovation in dense, sparse, and hybrid representation, including embedding models, contrastive learning, metric learning, learning-to-rank, and reinforcement learning to advance retrieval and recommendation at scale. Architect end-to-end AI systems utilizing LLMs, VLMs, multi-agent frameworks, and enterprise search designed for both offline training and online production workflows. Implement and adapt techniques from academic research; evaluate algorithmic trade-offs considering data requirements, latency, and runtime. Design industry-leading evaluation systems by combining LLM-as-judge, ensemble evaluation, offline benchmarks, A/B testing, and human-in-the-loop workflows that set the standard for how AI outputs are validated at enterprise scale. Analyze trade-offs across cost, latency, and accuracy while ensuring compliance with UX, privacy, legal, and security constraints. Work cross-functionally with data, research, and product teams to translate models into production-ready services.,representations

Requirements

14+ years of experience in AI/ML with a strong track record of technical leadership across multiple divisions or product areas, including shaping technical vision and roadmaps for ML organizations.
Bachelor's, Master's, or Ph.D. in Computer Science or a related field (or equivalent experience).
Deep expertise across NLP, Computer Vision, Deep Learning, and Generative AI.
Expert-level experience with LLM fine-tuning techniques (LoRA, QLoRA), prompt engineering, RAG/GraphRAG, vector databases, multi-agent orchestration, and agentic workflows.
Strong proficiency with modern ML frameworks (PyTorch, TensorFlow/Keras, JAX, Hugging Face, CUDA) and Python.
Extensive experience with ML platform engineering (MLOps/LLMOps) including distributed training, model serving, feature stores, experiment platforms, and observability systems across cloud platforms (AWS, GCP, Azure).
Solid grasp of data engineering including dataset management, feature engineering, labeling pipelines, data drift, and quality frameworks at scale.
Proven ability to mentor senior engineers and elevate technical standards across teams.

Nice To Haves

Recognized thought leader within and beyond the organization through publications at top-tier AI/ML conferences, open-source contributions, or participation in industry forums.
Demonstrated ability to drive innovation and champion emerging technologies across engineering organizations.
Experience building and shipping GenAI-first applications at enterprise scale.
First-principles mindset for challenging assumptions about how AI systems should be designed, evaluated, and deployed.

Responsibilities

Shape the technical vision and roadmap for GenAI initiatives that extract structured insights from brand knowledge and transform them into verified, on-brand content at scale.
Leverage foundational principles of NLP, Linguistics, and Computer Vision to architect multimodal systems that push the frontier of grounded content generation across text, image, and video.
Drive innovation in dense, sparse, and hybrid representation, including embedding models, contrastive learning, metric learning, learning-to-rank, and reinforcement learning to advance retrieval and recommendation at scale.
Architect end-to-end AI systems utilizing LLMs, VLMs, multi-agent frameworks, and enterprise search designed for both offline training and online production workflows.
Implement and adapt techniques from academic research; evaluate algorithmic trade-offs considering data requirements, latency, and runtime.
Design industry-leading evaluation systems by combining LLM-as-judge, ensemble evaluation, offline benchmarks, A/B testing, and human-in-the-loop workflows that set the standard for how AI outputs are validated at enterprise scale.
Analyze trade-offs across cost, latency, and accuracy while ensuring compliance with UX, privacy, legal, and security constraints.
Work cross-functionally with data, research, and product teams to translate models into production-ready services.