Applied AI Engineer — Early Career

About Us

We’re building toward a world where every company can become its own AI lab. Goaly is a stealth AI startup founded by ex-Meta Superintelligence Labs engineers and researchers. Our mission is to dramatically lower the cost, time, and talent barriers to building proprietary AI — and make each generation of models faster and cheaper to build than the last. Backed by leading AI investors and endorsed by frontier AI researchers and builders, we’re looking for exceptional new grads who want to work on hard, foundational AI systems problems with outsized ownership from day one.

About the Role

Join as a full member of the technical team from day one. You’ll own meaningful AI problems end-to-end — not just a narrow component — and work directly with the founding team across model post-training, RL environments, distributed systems, training or inference performance, backend services, APIs, SDKs, or developer tools. We move fast, give strong engineers real responsibility early, and encourage great work to become papers, open-source contributions, and production systems used by real customers. This role is designed for exceptional early-career engineers finishing or recently finished with a bachelor’s or master’s degree, or showing equivalent ability through what they’ve built. We look for raw technical strength, initiative, and unusual learning speed more than pedigree. That can show up through research, OSS, ambitious projects, internships, competitions, startup work, or products used by real people. Candidates with deeper research experience, including AI PhDs, may be a stronger fit for our AI Research Scientist — Early Career role.

Responsibilities

• Build applied AI projects end-to-end — from problem framing and prototyping to evaluation, production rollout, and iteration.
• Create agentic systems and applications, including agent harnesses, tool use, workflows, memory, and multi-step execution.
• Apply strong prompt, context, and loop engineering to make models more reliable, capable, and efficient on real-world tasks.
• Track frontier models, research, and AI-native developer tools; quickly test what is useful and turn new capabilities into product improvements.
• Design evaluations, datasets, and feedback loops to measure model and agent performance, diagnose failures, and guide iteration.
• Move fluidly across the stack depending on where the hardest problem is — from model APIs and inference to backend services, developer tooling, and product features.
• Use AI coding and development tools