So, what’s the role all about?

You'll work at the intersection of frontier AI capabilities and a real enterprise product. The field moves faster than any roadmap can track. Your job is to close that gap: turn new models, protocols, and agent patterns into working systems, prove what matters in realistic scenarios, and produce the insights and reference implementations that let product and engineering teams move with confidence.

You'll need to be comfortable with ambiguity, at home where best practices don't exist yet, and quick to move on when the results point elsewhere.

How will you make an impact?

• Build agentic systems and full-stack prototypes end-to-end, shipping a first version in days and learning from real use
• Track the frontier (new models, agent patterns, protocols like MCP) and turn the promising ones into working prototypes rather than slideware
• Design experiments that show whether a concept holds up, measured in quality, latency, cost, and user impact
• Work directly with internal users, product managers, and customers to validate concepts early and iterate quickly
• Turn validated prototypes into reference implementations and write-ups that product teams can build on, then hand off cleanly and move to the next bet
• Give product and platform teams concrete feedback on where AI capabilities shine and where they fall short
• Move between initiatives as priorities shift. What you learn on one bet compounds into the next

So, what’s the role all about?

You'll work at the intersection of frontier AI capabilities and a real enterprise product. The field moves faster than any roadmap can track. Your job is to close that gap: turn new models, protocols, and agent patterns into working systems, prove what matters in realistic scenarios, and produce the insights and reference implementations that let product and engineering teams move with confidence.

You'll need to be comfortable with ambiguity, at home where best practices don't exist yet, and quick to move on when the results point elsewhere.

How will you make an impact?

• Build agentic systems and full-stack prototypes end-to-end, shipping a first version in days and learning from real use
• Track the frontier (new models, agent patterns, protocols like MCP) and turn the promising ones into working prototypes rather than slideware
• Design experiments that show whether a concept holds up, measured in quality, latency, cost, and user impact
• Work directly with internal users, product managers, and customers to validate concepts early and iterate quickly
• Turn validated prototypes into reference implementations and write-ups that product teams can build on, then hand off cleanly and move to the next bet
• Give product and platform teams concrete feedback on where AI capabilities shine and where they fall short
• Move between initiatives as priorities shift. What you learn on one bet compounds into the next