Metr
Metr2h ago
New
USD 402048–578583/yr

Member of Technical Staff, Cyberforensics

United StatesUnited States·BerkeleyEmployeelead
OtherMember Of Technical Staff
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Quick Summary

Overview

About METR We are a nonprofit research organization that develops scientific methods to assess AI capabilities, risks, and mitigations,

Technical Tools
OtherMember Of Technical Staff

We are a nonprofit research organization that develops scientific methods to assess AI capabilities, risks, and mitigations, with a specific focus on threats related to AI R&D automation and misalignment.

We believe it is robustly good for policymakers and civil society to have a clear understanding of risks from AI systems, and we are extremely excited to build a team of ambitious, excellent people to tackle one of the most important challenges of our time.

METR has started embedding researchers inside frontier labs to investigate incidentsstress-test labs’ internal agent monitoring systems, and assess loss-of-control risks from internal deployment. As agent capabilities increase, we expect this to be one of the most important sources of independent information the world has about catastrophic risks from advanced AI. 

Recent incidents have involved complex multi-day cyber attacks on frontier lab internal infrastructure and external third parties. As we further develop our incident investigation and embedded stress-testing capacity, we will need talented cyberforensics researchers who can conduct embedded exercises. We expect these assessors to have deep access, and for their work to be a large part of METR's impact in the next year. We want to build on the momentum from previous exercises to further develop our risk assessments.

  • Incident investigation: You'll be embedded in a frontier AI lab for up to several weeks at a time, likely alongside 1-4 other METR staff. Between exercises, you'll practice, develop the general methodology, talk to other researchers, build tooling to make future exercises go better, help us hire and scale, write up results, and plan/coordinate future exercises.

  • Red-teaming: You will attack agent monitoring and security systems, potentially embedded in labs or red-teaming METR internal infrastructure.

  • Reporting: You'd produce findings rigorous enough for lab boards, governments, and the public and contribute to METR's public incident tracking and risk reports.

  • Building AI-assisted forensic tooling: Incidents at our scale (tens of thousands of actions) often can't be read solely by hand. You'd build LLM-powered pipelines to triage transcripts, cluster behaviors, flag deception, and accelerate future investigations.

  • Digital forensics and incident response: You have investigated severe security incidents end to end. You have experience with evidence acquisition and preservation, log and timeline reconstruction across cloud, network, endpoint, and identity systems, attacker tradecraft analysis, and post-incident reporting.

  • Cloud and infrastructure fluency: You can follow an intrusion through AWS (CloudTrail, IAM, VPC flow logs), Kubernetes and containers, CI/CD, and package registries.

  • Understanding LLMs: You know how frontier models are trained and deployed (RL post-training, agent scaffolds, sandboxing, monitoring) well enough to reason about root causes, and you build and analyze with LLMs.

  • Attention to detail and communication: You can run rigorous investigations and write findings that hold up to scrutiny.

  • Experience investigating incidents involving AI agents, or research on agent misbehavior, deception, or sandbox escapes.

  • Exploit and vulnerability analysis.

  • Experience with training-data analysis, model internals/interpretability, or running experiments on model checkpoints.

  • Formal investigation experience: NTSB/CSB-style safety investigations, law enforcement or intelligence forensics, regulatory or expert-witness work.

  • Familiarity with the tooling in our environment: DataDog, Kubernetes, CrowdStrike Falcon, Okta, Tailscale, Pulumi, PostgreSQL.

  • Our Culture
     
    METR is a mission-driven organization. We believe our work can meaningfully shape humanity's future for the better, and we want to be the best people in the world doing this work. We have a tight-knit, collaborative research culture rooted in truth-seeking and integrity. We're fiercely committed to producing high-quality, trustworthy science. We're honest and transparent about our results, especially when they may go against the grain. We've earned trust as reliable partners who handle confidential information with care. We maintain a low-ego, drama-free environment focused on what matters.
     
    Hybrid Preferred: Our technical team members are in our office in Berkeley 3-5 days/week. We would ideally like for you to be in person too, but we are happy to be flexible here. If you lack US work authorization and would like to work in-person, we can likely sponsor a cap-exempt H-1B visa for this role.
     
    We encourage you to apply even if your background may not seem like the perfect fit! We would rather review a larger pool of applications than risk missing out on a promising candidate for the position.
     
    We are committed to diversity and equal opportunity in all aspects of our hiring process. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We welcome and encourage all qualified candidates to apply for our open positions.

    Location & Eligibility

    Where is the job
    Berkeley, United States
    On-site at the office
    Who can apply
    US

    Listing Details

    Posted
    August 26, 2026
    First seen
    August 27, 2026
    Last seen
    August 27, 2026

    Posting Health

    Days active
    0
    Repost count
    0
    Trust Level
    71%
    Scored at
    August 27, 2026

    Signal breakdown

    freshnesssource trustcontent trustemployer trust
    Metr
    Metr
    lever
    Employees
    5
    Founded
    2022
    Domain
    metr.org
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    MetrMember of Technical Staff, CyberforensicsUSD 402048–578583