Cyber Evaluations Engineer
- 雇用形態
- Full Time
- 勤務形態
- Remote
- 給与
- 記載なし
この仕事について
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About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
We're hiring Cyber Evaluations Engineers to build and run the evaluations that measure cyber-relevant capabilities and safeguard robustness in our models. You'll design new evals, run per-release robustness testing, and dig into data on jailbreaks and prompt bypasses to understand where our safeguards hold up and where they don't.
You'll also design many of the probes that detect cyber abuse in production and help shape the overall detection architecture alongside the policy team.
Key responsibilities
- Design and run capability, uplift, and safety evaluations to assess cyber-relevant risk in new models
- Execute per-release safeguard-robustness testing ahead of major model launches
- Analyze evaluation results and communicate findings clearly to the team and to stakeholders
- Design, prototype, and tune detection probes for cyber misuse
- Work with the cyber policy team to turn policy lines into a layered, robust abuse-detection architecture, and measure its precision and coverage over time
- Build and maintain internal tooling used to run and score evaluations
- Collaborate with policy and engineering partners to translate eval findings into safeguard improvements
Minimum qualifications
- Experience building or running evaluations, benchmarks, or test suites for software or ML systems, including delivering results on short, fixed timelines
- Hands-on cybersecurity experience (e.g., CTF participation, vulnerability research, exploit development, or security research)
- Proficiency in Python
- Strong ability to communicate evaluation results with multiple cross-functional stakeholders or potential policy stakeholders
Preferred qualifications
- Deep offensive-security or security-research experience, including experience building AI security benchmarks
- Experience analyzing adversarial or abuse data (e.g., jailbreaks, prompt bypasses, intrusion or fraud telemetry)
- Experience working onsite with government partners on testing or evaluation engagements
- Experience with AI/ML evaluation frameworks
- Familiarity with coordinated vulnerability disclosure practices
- Experience testing pre-release or pre-deployment software or models under confidentiality constraints
- Experience authoring detection content (e.g., Sigma, YARA, Suricata, SIEM rules) or building ML-based abuse detection
- Active secret security clearance or higher, or eligibility to obtain one
- The annual compensation range for this role is listed below.
- For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
- Annual Salary:
- $300,000 — $405,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.
We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day.
If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science.
We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
主な業務内容
- Design and run capability, uplift, and safety evaluations to assess cyber-relevant risk in new models
- Execute per-release safeguard-robustness testing ahead of major model launches
- Analyze evaluation results and communicate findings clearly to the team and to stakeholders
- Design, prototype, and tune detection probes for cyber misuse
- Work with the cyber policy team to turn policy lines into a layered, robust abuse-detection architecture, and measure its precision and coverage over time
- Build and maintain internal tooling used to run and score evaluations
- Collaborate with policy and engineering partners to translate eval findings into safeguard improvements
応募要件
- Experience building or running evaluations, benchmarks, or test suites for software or ML systems, including delivering results on short, fixed timelines
- Hands-on cybersecurity experience (e.g., CTF participation, vulnerability research, exploit development, or security research)
- Proficiency in Python
- Strong ability to communicate evaluation results with multiple cross-functional stakeholders or potential policy stakeholders
- Preferred qualifications: Deep offensive-security or security-research experience, including experience building AI security benchmarks
- Preferred qualifications: Experience analyzing adversarial or abuse data (e.g., jailbreaks, prompt bypasses, intrusion or fraud telemetry)
- Preferred qualifications: Experience working onsite with government partners on testing or evaluation engagements
- Preferred qualifications: Experience with AI/ML evaluation frameworks
- Preferred qualifications: Familiarity with coordinated vulnerability disclosure practices
- Preferred qualifications: Experience testing pre-release or pre-deployment software or models under confidentiality constraints
- Preferred qualifications: Experience authoring detection content (e.g., Sigma, YARA, Suricata, SIEM rules) or building ML-based abuse detection
- Preferred qualifications: Active secret security clearance or higher, or eligibility to obtain one
- Preferred qualifications: The annual compensation range for this role is listed below.
- Preferred qualifications: For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.