), to prepare for risks from scientific superintelligence. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a safety research agenda that explores longer-term needs such as oversight of superintelligent scientific systems.

We’re seeking a Technical Mitigations Lead, to lead the build out of safety systems at Lila for the safe deployment of our scientific capabilities to the world. Given the novelty of Lila’s workflows, integrating frontier-class language models with narrow scientific tools and lab-based automation, this role will require the design and deployment of technical safeguards beyond the current state-of-the-art. We expect the person in this role to start off the initial mitigations build-out, and then slowly build a team to support this function.

What You'll Be Building 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. 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 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. 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, Contributing to broader, high-quality research efforts - as and when needed - for scientific capability evaluation and restriction.

Contribute to external communications on Lila’s safety efforts. What You’ll Need to Succeed Track record of building safety systems, classifiers, or conducting post-training for frontier-class problems - science, reasoning, programming, etc. 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.

Bonus Points For Experience in developing or applying ML to biological or physical sciences Experience in building safeguards for scientific risks for frontier models / narrow scientific tools.

Compensation

We offer competitive base

with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. S.

Benefits

program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter

, including bike share memberships for office based employees; and a company subsidized lunch program.

program tailored to their region. -based positions; international salaries are set to local market.

Staff / Principal Research Engineer, AI Safety, Technical Mitigations