Research Engineer (Senior Staff+), Safeguards Labs at Anthropic

Hybrid - San Francisco, CA; New York City, NY

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Anthropic’s Safeguards Labs team is building research and engineering solutions to protect Claude from misuse. The role requires a senior research engineer who can design and run offline analyses, build detection prototypes, and translate research into production safeguards. The position is hybrid, based in either San Francisco or New York City, and offers a competitive salary range of $350k–$850k USD with extensive benefits.

Salary

USD 350,000 - 850,000

Requirements

Skills

  • Have a track record of independently driving research projects from ambiguous problem statements to concrete results, ideally in AI, ML, security, integrity, or a related technical field.
  • Are comfortable scoping your own work and switching between research, engineering, and analysis as a project demands.
  • Have working familiarity with how large language models operate — sampling, prompting, training — even if LLMs aren't your primary background.
  • Are proficient in Python and comfortable working with large datasets.
  • Care about the societal impacts of AI and want your work to directly reduce real-world harm.

Responsibilities

  • Lead and contribute to research projects investigating new methods for detecting misuse of Claude, identifying malicious organizations and accounts, strengthening model safeguards, and other safety needs.
  • Design and run offline analyses over model usage data to surface abuse patterns, build classifiers and detection systems, and evaluate their effectiveness.
  • Develop and iterate on prototypes that could eventually feed signals into the real-time safeguards path, partnering with engineers on tech transfer.
  • Contribute to a broader research portfolio investigating methods for detecting abusive behavior in chat-based or agentive workflows, and for training the model to robustly refrain from dangerous responses or behaviors without over-refusing.
  • Build evaluations and methodologies for measuring whether safeguards actually work, including in agentic settings.
  • Write up findings clearly so they inform decisions across Trust & Safety, research, and product teams.

Technologies

PythonLarge language modelsLarge datasets

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