Applied AI Engineer

Pure Storage is reshaping the data storage industry with innovative technology. The Applied AI Engineer role combines technical expertise with business acumen to lead AI advisory engagements and drive AI deployment strategies for enterprise customers.

Responsibilities:

• Lead technical discovery and advisory engagements with customers to identify high-value AI use cases relevant to their industry and data
• Advise on end-to-end AI deployment — model selection, training/fine-tuning strategies, inference optimization, data pipeline design, and evaluation/alignment/safety frameworks — optimized for Pure's platform
• Collaborate cross-functionally to qualify and position AI opportunities, and develop proof-of-value prototypes that translate technical performance into business outcomes
• Create AI enablement content for field teams, partners, and customers — technical walkthroughs, qualification guides, workshops, and vertical-specific use case frameworks
• Present at industry conferences, publish technical content and peer-reviewed research, and represent Pure in engagements with strategic technology partners (NVIDIA, AMD, cloud providers, MSPs)
• Operate autonomously in ambiguous environments — independently scoping high-impact initiatives and driving them to completion at the right pace

Requirements:

• Advanced degree in a quantitative field (Computer Science, Physics, Mathematics, Engineering, or related), or equivalent demonstrated through publications and production system experience
• 8+ years building and deploying AI/ML systems in cloud or on-prem environments
• Deep expertise in modern AI/ML — large language models, distributed training, inference optimization, agentic systems, evaluation/alignment frameworks, and classical ML
• Fluency with the modern AI stack (PyTorch, vLLM, Ray, Kubernetes) and data platforms (Spark, Snowflake, Kafka, etc.)
• Able to scope and carry out research projects that support critical business objectives, both independently and collaboratively
• Excellent written, verbal, and presentation skills — equally clear with hands-on data scientists and C-suite decision-makers
• Experience with large-scale AI infrastructure: GPU clusters, high-performance storage, containerized deployments
• Experience in customer-facing technical advisory or consulting roles with enterprise accounts
• Experience leading independent, multi-year research from concept to publication or large-scale production deployment
• Exposure to multiple industry verticals (financial services, healthcare, telco, manufacturing)
• Publication record, conference presentations, or recognized technical presence
• Background combining applied research with shipping production systems
• Familiarity with CRM/opportunity management systems (Salesforce preferred)