Staff / Principal Research Engineer, AI Safety, Technical Mitigations

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · AI

This role focuses on building and implementing AI safety strategies and systems for the safe deployment of scientific capabilities, involving technical safety strategy development, safety-focused evaluations, and a safety research agenda. It requires experience in building safety systems, classifiers, or post-training for frontier-class problems and scalable production systems.

What you'd actually do

  1. Set the build and research strategy for Lila’s safety systems, across scientific data analysis and generation pipelines, safety post-training, refusal classifiers, automated safety-testing / red-teaming systems, and monitoring systems.
  2. Conduct initial safeguards experimentation and buildout for Lila’s specific scientific needs, and subsequently lead a small team to execute on the build and research agenda
  3. Lead safety systems research to iterate Lila’s systems beyond the state of the art, given the needs of technical safeguards for both in silico and lab-based scientific workflows.
  4. Partner closely with Other members of the safety team, such as domain-specific experts (bio, chem, materials) and eval buildout teams, and Non-safety teams, such as core AI, lab automation, and product teams,
  5. Contributing to broader, high-quality research efforts - as and when needed - for scientific capability evaluation and restriction.

Skills

Required

  • Track record of building safety systems, classifiers, or conducting post-training for frontier-class problems
  • 4-6+ years working in technically engineering with ML systems.
  • Experience building scalable, production systems, not just prototypes.
  • Demonstrated ability to set research directions for open problems in post-training, classifier buildouts, and other relevant systems.
  • Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.

Nice to have

  • Experience in developing or applying ML to biological or physical sciences
  • Experience in building safeguards for scientific risks for frontier models / narrow scientific tools.
  • Demonstrated ability to lead teams towards engineering goals

What the JD emphasized

  • Track record of building safety systems, classifiers, or conducting post-training for frontier-class problems
  • Experience building scalable, production systems, not just prototypes.
  • Demonstrated ability to set research directions for open problems in post-training, classifier buildouts, and other relevant systems.

Other signals

  • AI Safety
  • Technical Mitigations
  • Frontier-class models
  • Safety systems