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

Amd

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$204,000–$306,000 / yr
Posted
117 days ago
Freshness
Confirmed live today
Closes
Jul 2, 2027
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $220k
This role $255k
$159k $322k
below market most similar roles pay here above market

This role pays more than 79% of similar roles. Most pay $185,900–$254,750 — the blue band above. At the midpoint, this role pays about $255k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 496 open roles on FindRole.

Listed pay typically runs $172,000–$258,000 across 496 roles with salary data.

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At a glance

TL;DR · AI Research Scientist, Recursive Self Improvement, AI Safety and Reinforcement Learning

Role Posting Log in to save

As an AI Research Scientist, Recursive Self Improvement, AI Safety and Reinforcement Learning, you will join the team to research recursive self-improvement in a bounded, engineering-first context. You will develop systems where models, data generators, or toolchains improve their own training signals, curricula, or verification under explicit governance and human oversight. Your daily work involves researching synthetic data quality, targeted self-play, and automated curriculum refinement while designing measurements to prevent bias or reward hacking. You will research self-improving training loops, iterative distillation, and self-critique while partnering with RL scientists on policy optimization. You must utilize machine learning, reinforcement learning, and AI safety expertise to build controlled experimental harnesses. This role addresses capability drift and distributional shift within AI-for-HW and generative-AI programs.

Skills

Reinforcement Learning Machine Learning AI Safety generative-AI Iterative Distillation Synthetic Data Policy Optimization Preference Learning

What you'll do

  • Research self-improving training loops including model-generated supervision, iterative distillation, and automated curriculum updates.
  • Develop theory- and systems-grounded evaluations for capability drift, Goodhart effects, and distributional shift in closed-loop training.
  • Partner with RL scientists to align RSI-style objectives with policy optimization and preference learning.
  • Define red-team protocols and monitoring systems for RSI pilots.
  • Document rollback criteria before experiments interact with shared infrastructure.
  • Publish technical reports and align internal narratives with responsible deployment standards.
  • Build controlled experimental harnesses and reproducible RSI microcosms using software skills.

What we're looking for

  • Research self-improving training loops including model-generated supervision, iterative distillation, and automated curriculum updates.
  • Develop theory- and systems-grounded evaluations for capability drift, Goodhart effects, and distributional shift in closed-loop training.
  • Partner with RL scientists on the intersection of RSI-style objectives with policy optimization and preference learning.
  • Define red-team protocols and monitoring for RSI pilots and document rollback criteria.
  • Publish or produce technical reports aligned with responsible deployment standards.
  • Strong background in ML, AI safety, or reinforcement learning with publications or work in iterative training (preferred).
  • Experience with empirical safety evaluation, scalable oversight, or stress-testing generative model training pipelines (preferred).
  • Strong software skills for building controlled experimental harnesses and reproducible RSI microcosms (preferred).
  • PhD in Computer Science, Machine Learning, or a related field (preferred).

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