layiq

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Member of Technical Staff - Mechanistic Interpretability at V max | LAYIQ

Develop methods for using mechanistic interpretability to extract useful training signals from the internal states of language models.
Turn representations, features, circuits, and causal model behaviors into intrinsic rewards for reinforcement learning.
Compare interpretability-derived rewards against human feedback, learned reward models, verifiers, and task-level outcome rewards.
Design metrics and baselines for reward quality, including alignment with intended behavior, generalization across tasks, robustness, and resistance to reward hacking.
Investigate how internal representations evolve during RL and post-training, and use these insights to improve training objectives.
Develop infrastructure for reproducible, large-scale experiments on LLM agents, interpretability tools, and RL environments.
Define and pursue a high-impact research agenda that advances Vmax’s goal of open-ended learning beyond imitation of human expertise.
Minimum Requirements
PhD or equivalent experience in machine learning, reinforcement learning, or a closely related field.
Track record of research excellence, as demonstrated by publications, open source work, deployed AI systems, or other substantial technical contributions.
Deep understanding of modern machine learning, especially reinforcement learning, representation learning, and large language models.
Strong familiarity with LLM post-training methods
Experience designing and running rigorous ML experiments, including ablations, baselines, evaluation design, and failure analysis.
Expertise with Python and at least one major ML framework such as PyTorch or JAX.
Ability to work independently on open-ended research problems and turn ambiguous ideas into concrete experimental programs.
Nice to have
Experience with mechanistic interpretability techniques such as activation patching, probing, sparse autoencoders, feature attribution
Experience training or evaluating language-model agents in interactive, tool-using, or multi-step reasoning settings.
Familiarity with scalable RL infrastructure, distributed training, experiment tracking, and large-scale evaluation pipelines.
Experience developing reward models, verifiers, process supervision methods, or automated evaluation systems.
Demonstrated software engineering ability, especially in research codebases that require reliability, reproducibility, and iteration speed.
Ability to present technical results and their strategic implications to both research and non-research audiences.
Role specific location policy
This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement

Member of Technical Staff - Mechanistic Interpretability

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