Founding AI Researcher — RL & Agentic AI

Goaly AI; Palo Alto (CA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Visa sponsorship

Meals & snacks

Hybrid in Palo Alto

Job summary

Goaly AI in Palo Alto is seeking a research‑engineering contributor to shape the experimental loop that turns a capable base model into a useful agent. You will design tasks, prepare training and evaluation data, run reinforcement learning experiments, diagnose model behavior, and convert results into better recipes and production models.

This is a research‑engineering role blending hypothesis formation with high‑quality code, training pipelines, and cross‑functional collaboration with RL

Qualifications

• Strong Python and software‑engineering skills, including turning ambiguous ideas into reliable experimental systems.
• Hands‑on experience training, fine‑tuning, or evaluating modern language models, or related ML research area.
• Solid understanding of deep learning, optimization, and reinforcement learning intuition.

Responsibilities

• Design and run post-training experiments for agentic capabilities, including tool use, coding, reasoning, planning, long-horizon task completion, and recovery from failure.
• Prepare high-quality training and evaluation data: define task distributions, curate and filter examples, control contamination, balance difficulty, and build reproducible data-generation pipelines.
• Build realistic RL environments and task harnesses with clear interfaces, reliable resets, isolated execution, useful telemetry, and reward signals that are hard to game.
• Develop evaluations that measure both capability and reliability. Create regression suites, behavioral slices, error taxonomies, and dashboards that connect aggregate metrics to concrete model failures.
• Iterate on training recipes, including supervised warm starts, sampling strategies, reward design, verifiers, curricula, optimization choices, and reinforcement fine-tuning methods.
• Analyze trajectories and model behavior to find reward hacking, shortcut learning, mode collapse, distribution gaps, and other failure modes; turn those findings into targeted experiments.
• Improve the research workflow through better experiment configuration, rollout inspection, reproducibility, checkpoint evaluation, and automated comparison of runs.
• Partner with systems engineers to debug cross-layer problems in rollout inference, environment execution, distributed training, and data movement.
• Translate successful research ideas into stable, repeatable pipelines and help set the team’s longer‑term post‑training roadmap.

Skills

Python

Machine learning

Experimentation

Communication

Tools

PyTorch

JAX

Distributed ML systems

Job description

Goaly AI in Palo Alto is seeking a research‑engineering contributor to shape the experimental loop that turns a capable base model into a useful agent. You will design tasks, prepare training and evaluation data, run reinforcement learning experiments, diagnose model behavior, and convert results into better recipes and production models.

This is a research‑engineering role blending hypothesis formation with high‑quality code, training pipelines, and cross‑functional collaboration with RL