Projects are mentor-guided. By default, you will be given a list of research proposals and match you with one based on your research interests and background. You’ll meet weekly with a Scale researcher who sets the direction and gives feedback, and you will drive the day-to-day work.

Own research end to end: Turn a high-level safety question into hypotheses, baselines, and controlled experiments, and produce work that others can inspect and build on.

Build the measurement: Implement and debug evaluation harnesses, agent environments, datasets, monitoring methods, or post-training experiments.

Exercise research judgment: Identify confounds, separate what a model can do from what it tends to do, make sense of negative results, and decide what to try next without step-by-step direction.

Make the work legible: Produce reproducible code, experiment logs, analysis, and precise technical writing. External outputs, where appropriate, remain subject to research, security, and communications review. Outputs also include research papers, which will be submitted to top ML conferences (NeurIPS, ICML, ICLR).

Who We're Looking For

You may be a good fit if you:

are motivated by ensuring the safe and responsible development of advanced AI systems;

are interested in technical safety research, such as alignment evaluations, AI control, monitoring, multi-agent safety, or open-weight model safety;

are pursuing or have completed a PhD in CS, ML, statistics, mathematics, or a related field (we also encourage researchers with an equivalent track record to apply);

can make progress with high-level guidance, including implementing, debugging, getting unstuck, interpreting results, and proposing next experiments;

communicate research clearly and handle confidential information and internal systems responsibly.

Safety Research Scholar - Human Frontier Labs

Responsibilities

Company

Scale AI

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