Senior Applied AI Solutions Engineer

Responsibilities

• The product and sales teams have a library of working, polished demos they reach for on calls
• Enterprise customers you've touched have meaningfully faster time-to-value than those you haven't
• At least 2–3 product changes were shipped because of feedback you originated
• The team understands where applied AI is heading 6–12 months from now, partly because you told them
• Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams
• Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months
• Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations
• Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"
• Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale

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