Applied AI Engineer

About the Company Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native.

It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%.

That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.

About the Role

As an Applied AI Engineer, you'll turn model capability into real product behavior. You'll own problems end-to-end — from shaping model behavior, to building the systems around it, to making sure it holds up in production.

This role sits at the intersection of machine learning, systems, and product: making AI actually work for users, not just in demos.

What You'll Work On Build and ship AI features end-to-end (model system user experience) Design and iterate on prompts, tools, memory, and agent workflows Turn raw model outputs into structured, reliable, predictable behavior Debug issues across the full stack — model, orchestration, infra, UX Optimize for latency, cost, and production reliability Develop lightweight evaluation frameworks to measure real-world performance Work closely with product and engineering to turn ambiguous problems into working systems Requirements Strong foundation in machine learning and modern neural network architectures Hands-on experience training, fine-tuning, or deploying ML models Ability to write clean, production-quality code Comfort working across abstraction layers — model, infra, product Strong problem-solving skills in ambiguous, fast-moving environments A bias toward shipping, iteration, and continuous improvement Tech Stack P