Applied AI Engineer

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Logo of Pure Storage

Pure Storage

1001 - 5000 employees

Founded 2010

🏢 Enterprise

Enterprise • Cloud • Data Storage

Pure Storage is a leader in the field of data storage platforms, renowned for its innovative solutions in the industry. It has been consistently recognized as a leader in the Gartner Magic Quadrant for both Primary Storage Platforms and File and Object Storage Platforms, achieving the highest marks in Execution and Vision. Pure Storage provides scalable and sustainable storage solutions that cater to both enterprise and cloud needs, emphasizing zero downtime and seamless performance. It offers a range of services including AI infrastructure, cloud deployments, and data protection, making it a critical player in modern data management.

📋 Description

• 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) • 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.

🏖️ Benefits

• Flexible time off • Wellness resources • Company-sponsored team events

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