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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI deploymentmodel selectiontraining strategiesinference optimizationdata pipeline designevaluation frameworkslarge language modelsdistributed trainingclassical ML

Soft Skills

collaborationpresentation skillswritten communicationverbal communicationindependent research