layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

AI Research Scientist, Recursive Self Improvement, AI Safety and Reinforcement Learning at AMD | LAYIQ

Responsibilities

THE ROLE:

We are hiring an AI Research Scientist, Recursive Self Improvement, AI Safety and Reinforcement Learning focused on recursive self-improvement (RSI) in a bounded, engineering-first sense: systems where models, data generators, or toolchains participate in improving their own training signals, curricula, or verification—always under explicit governance, kill switches, and human oversight. You will research when such loops help (e.g.

synthetic data quality, targeted self-play, automated curriculum refinement) versus when they amplify bias or reward hacking, and you will design measurement and containment so RSI-style pipelines remain auditable and safe for AMD’s AI-for-HW and generative-AI programs.

THE PERSON:

You are skeptical by default but constructive: you formalize assumptions, bound autonomy, and insist on counterfactual evaluation. You connect RSI concepts to concrete metrics—data efficiency, robustness, regression rates—not open-ended capability claims.

KEY RESPONSIBILITIES:

Research self-improving training loops: model-generated supervision, iterative distillation, self-critique, and automated curriculum updates with clear scope limits
Develop theory- and systems-grounded evaluations for capability drift, Goodhart effects, and distributional shift in closed-loop training
Partner with RL scientists on where RSI-style objectives intersect policy optimization and preference learning
Define red-team protocols and monitoring for RSI pilots; document rollback criteria before experiments touch shared infrastructure
Publish or produce technical reports where appropriate; align internal narrative with responsible deployment standards
PREFERRED EXPERIENCE: Strong background in machine learning (ML), AI safety, reinforcement learning, or a related field, with publications or substantial work in iterative training, self-training, or open-ended learning.
Experience with empirical safety evaluation, scalable oversight, or stress-testing of generative model training pipelines
Strong software skills for building controlled experimental harnesses and reproducible RSI microcosms
ACADEMIC CREDENTIALS: PhD in Computer Science, Machine Learning, or related field strongly preferred.

AI Research Scientist, Recursive Self Improvement, AI Safety and Reinforcement Learning

Employer job description

WHAT YOU DO AT AMD CHANGES EVERYTHING

THE ROLE:

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KEY RESPONSIBILITIES:

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Responsibilities

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Qualifications

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