AI Engineer

a16z-speedrun; Seattle (WA); On-site; USD 150,000 - 230,000; Full time

Job summary

Kanu AI, Seattle-based AI startup, is hiring an AI engineer to own the AI systems at the heart of the product. You’ll work directly with the founders and manage the full loop: data, training, evaluations, and shipping into production.

You will build and own the ML stack—from data pipelines to evaluations and deployment—ship features continuously, and iterate against real usage. You’ll track research frontiers and apply practical solutions where they add real value.

Qualifications

• Shipped ML in production, research, or serious projects.
• Strong fundamentals across ML stack, not just notebooks.
• Pragmatic, prefer reliable approaches over clever but risky ones.
• Comfort owning end-to-end systems with no dedicated ML team.

Responsibilities

• Build and own the ML stack—data pipelines, infrastructure, agent optimization, evaluations, tuning.
• Ship AI features into production continuously and iterate based on usage.
• Improve evaluation harnesses to measure real product improvement.
• Track research frontier and apply it where it wins.

Skills

ML engineering

Data pipelines

Production ML

Evaluation frameworks

Tools

Python

Cloud infrastructure

Job description

About the Company

Kanu AI is your organization’s AI coworker that deploys in your own cloud, continuously learns from your data, team intelligence, and outputs, and uses that knowledge to encode and maintain workflows that mirror how your people work.

The Role

We're hiring an AI engineer to own the AI systems at the heart of the product. You'll work directly with the founders and own the full loop: data, training, evaluations, and shipping into production.

What You'll Do

• Build and own the ML stack—data pipelines, infrastructure, agent optimization, evaluations, tuning

• Ship AI features into production constantly and iterate against real usage

• Improve the evaluation harnesses that tell us whether the product is actually getting better

• Track the research frontier and apply it pragmatically—novelty only where it wins

What We're Looking For

• Evidence you've shipped ML that real users touched—in production, research, or serious projects

• Strong fundamentals across the stack, not just notebooks

• Pragmatism: you pick the boring approach that works over the clever one that might

• Comfort owning systems end to end with no ML team behind you

Compensation & Equity

Early-stage compensation: a meaningful equity stake plus salary. Specifics depend on experience and location—we're happy to talk through the details early in the process.