Member of Technical Staff; AI​/ML

What you will do

• Partner with product, operations, and engineering teams to understand business workflows, identify where AI can create leverage, and turn ambiguous problems into shipped products.
• Design, prototype, evaluate, deploy, and iterate on AI systems using LLMs, agents, retrieval, classification, extraction, prediction, automation, and other ML techniques where appropriate.
• Turn large-scale historical data, operational data, and continuously generated service data into production systems that improve decisions, automate work, reduce cost, and increase throughput.
• Build reliable AI-powered products and internal platforms with attention to latency, cost, accuracy, observability, reliability, UX, compliance, and operational constraints.
• Work directly with messy, incomplete, and legacy data, getting close to the underlying workflows and edge cases.
• Make pragmatic technical decisions that balance frontier capability with speed, simplicity, and measurable business impact.

• AI-native development practice, including hands‑on experience orchestrating coding agents like Codex or Claude Code to plan, write, and ship production code.
• Strong experience building and shipping software, AI systems, or ML-powered products that created measurable value for users, customers, operations, or the business.
• Deep familiarity with modern AI patterns, including LLMs, retrieval-augmented generation, agents, tool use, model evaluation, prompt engineering, fine-tuning, structured outputs, and production AI system design.
• Strong product instincts, including the ability to identify where AI is useful, where it is not, and how to make tradeoffs across accuracy, cost, latency, reliability, and user experience.
• Strong programming and production engineering ability, especially in Python. Experience with Go, Type Script, cloud environments, ML frameworks, vector databases, orchestration tools, or modern LLM APIs is a plus.
• Evidence that you can turn technical depth into real-world outcomes. Advanced degrees, publications, or research experience are welcome, but shipped systems, user adoption, workflow lift, cost reduction, or revenue impact matter most.