Manager / Senior Manager, AI Safety Data and Evaluation Program

Lila Sciences • Cambridge, MA

4d • $180,000 - $256,000

About The Position

Lila Sciences is building a talent-dense AI safety team to work across the organization and identify and mitigate risks from scientific superintelligence while preserving scientific discovery capabilities. The team’s initial focus is to develop and implement a safety strategy tailored to Lila’s goals and deployment strategies, encompassing technical safety strategy, ecosystem engagement, safety-focused evaluations, risk-mitigation systems, and longer-term research needs such as oversight of superintelligent scientific systems.

The Manager / Senior Manager will turn scientific risk questions into well-run expert-data and evaluation programs. The role connects internal risk mapping and threat modeling with expertise from Lila teams and external specialists. Its primary focus is program execution and data quality: coordinating contributors, running red-teaming and evaluation campaigns, and ensuring outputs meet agreed standards. The role also gathers targeted feedback from AI Safety community partners and works with scientists who frame research questions and interpret their significance. The resulting evidence will inform safety decisions made by Lila senior leadership.

This position translates scientific briefs into executable plans, sources and coordinates experts, manages contributor onboarding and support, runs data collection and evaluation programs, establishes quality controls, maintains records and datasets, and escalates questions requiring scientific judgment. The successful candidate will have experience delivering specialist-contributor programs, a substantial working understanding of AI safety, sound operational judgment, strong quality-management and communication skills, and sufficient technical and scientific fluency to work productively with subject-matter experts.

Requirements

• Experience delivering expert-data, research-operations, evaluation, or similarly demanding programs involving specialist contributors.
• A substantial working understanding of AI safety, including how threat models, capability evaluations, red teaming, and safeguards inform one another.
• Ability to interpret a safety research brief, recognize its assumptions and the consequences of data-quality failures, and identify when scientific escalation is needed.
• Evidence of applying AI safety understanding in research, evaluation, or expert-data work, including how it shaped program scoping, execution, or review.
• Strong operational judgment and ability to manage dependencies, changing requirements, and several parallel workstreams.
• Track record of setting measurable quality standards, detecting recurring problems, and improving data-production processes.
• Clear communication, careful documentation, and ability to build effective relationships with researchers, experts, and vendors.
• Sufficient technical and scientific fluency to understand research briefs, clarify ambiguity, and work productively with subject-matter experts.

Nice To Haves

• Experience contributing to scientific study design, assessing scientific validity, or developing evaluation rubrics.
• Deeper scientific expertise or research experience in biology, chemistry, or materials science.
• Experience with data analysis, scripting, annotation platforms, or tools for tracking research workflows and data provenance.

Responsibilities

• Translate agreed scientific briefs into executable plans specifying tasks, contributor needs, instructions, milestones, quality standards, and delivery expectations.
• Source and coordinate external experts with scientific colleagues and organize access to relevant expertise within Lila.
• Manage contributor onboarding, task assignment, training, and support.
• Run expert-data collection, annotation, red-teaming, and evaluation programs; track progress and resolve delivery problems.
• Establish quality controls for completeness, consistency, provenance, and adherence to rubrics.
• Coordinate calibration, review, disagreement resolution, and rework with scientific owners.
• Maintain clear records, versioned guidance, and usable datasets, and improve workflows using quality findings and researcher feedback.
• Identify questions requiring scientific judgment and bring them to the appropriate specialists, ensuring decisions are reflected in program materials and subsequent work.
• Gather targeted, high-impact feedback from AI Safety community partners on aspects of Lila’s safety approach.
• Generate evidence that informs safety decisions made by Lila senior leadership.

Benefits

• Competitive base compensation with bonus potential and generous early-stage equity.
• Medical, dental, and vision coverage for full-time U.S. employees.
• Employer-paid life and disability insurance for full-time U.S. employees.
• Flexible time off and generous company-wide holidays.
• Paid parental leave.
• Educational assistance program.
• Commuter benefits, including bike share memberships for office-based employees.
• Company-subsidized lunch program.
• Regionally tailored benefits for full-time employees outside the U.S.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: No Education Listed