Senior AI Engineer

Use AI to accelerate design, build, test, and iteration cycles

Prototype quickly, then harden systems for enterprise-grade reliability

Build evaluation frameworks, feedback loops, and guardrails to improve agents over time

Design prompts, retrieval pipelines, and orchestration logic that perform at scale

Make clear trade-offs on what to build, cut, or skip based on customer value

Partner with Product and Design to define capabilities that deliver real outcomes

Stay close to customer workflows and optimize for highest-impact problems

Identify capability gaps and unblock team progress proactively

Raise the quality bar through code review, design feedback, and pairing

Create reusable abstractions, patterns, and tooling that increase team velocity

Share learnings across the team and establish engineering best practices

You’re a strong software engineer who’s built your skills for an AI-native world. These principles resonate with you:

Bias to building: You move fast and resolve uncertainty by shipping

AI-native instincts: You treat LLMs, agents, and automation as core building blocks

Strong product judgment: You decide what matters and why—not just how to implement it

Learning velocity: You learn quickly from feedback and adjust based on data

Grounded optimism: You improve what's broken today and push toward what's possible next

End-to-end ownership: You understand production systems and own outcomes

We care more about capability and trajectory than years on a resume, but most strong candidates have:

3–6+ years shipping production software in complex, real-world systems

Strong command of TypeScript, Python, and Postgres

Shipped LLM-powered features serving real production traffic

Built retrieval pipelines and agent orchestration systems

Implemented evaluation frameworks for model outputs and agent behavior

Worked with vector databases, embedding models, and RAG architectures

Hands-on experience with modern LLM APIs (OpenAI, Gemini, Anthropic) and agent frameworks

Comfortable operating in ambiguity and taking responsibility for outcomes

Enterprise-grade reliability: Building systems professionals depend on

Human-in-the-loop design: Knowing when to automate vs. when to surface decisions

Nuanced evaluation: Audits require judgment, so feedback structures matter

Explainability: Making AI outputs and reasoning transparent and trustworthy

Complex domains: Navigating compliance and enterprise rigor while moving fast

Shipping daily value: Delivering agent experiences customers use every day

Fearless — Inspire and break down seemingly impossible walls

Fast — Launch fast with excellence; iterate to perfection

Lovable — Deliver happiness and 11-star experiences

Owners — Execute and run the business with ownership

Win-win — Create mutual value and earn trust for life

Inclusive — Scale the best ideas with inclusive teams