AI Research Scientist

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 MSL 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

You will own research at the intersection of agentic reinforcement learning, post-training, evaluation, environments, and scaling. You will identify high-leverage questions, design and run decisive experiments, and translate results into model improvements and reusable systems.

This role is designed for researchers completing or recently completing a PhD, as well as candidates with an equivalent record of original research. It is not a purely academic position: strong candidates write excellent code, work closely with systems engineers, and care whether an idea survives realistic evaluation and production constraints.

What you'll do

• Formulate high-leverage research questions about agentic capability and reliability, RL algorithms, reward and verifier design, exploration, curricula, environment design, task distributions, and scaling behavior.

• Design rigorous experiments, ablations, controls, and evaluations that separate real model improvement from noise, data leakage, reward hacking, or benchmark overfitting.

• Implement new methods in modern deep-learning frameworks and integrate them with production training, rollout, environment, and evaluation systems.

• Build or improve datasets, agent environments, verifiers, and evaluations for domains such as coding, tool use, reasoning, long-horizon tasks, or computer interaction.

• Analyze trajectories and model behavior, develop useful failure taxonomies, and turn observations into testable hypotheses and prioritized experiments.

• Partner closely with Post-Training, RL Systems, Training, and Backend & Product engineers to scale promising ideas and expose them to realistic product constraints.

• Communicate findings in clear internal documents and technical reviews; contribute to papers, technical reports, blog posts, or open-source releases when aligned with company goals.