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.
We are seeking a Detection and Response Engineering Manager to lead our Detection and Response teams in creating comprehensive Security Observability, Detection Lifecycle, and Security Incident Response programs for Anthropic. You will collaborate closely with teams and leaders across Anthropic, focusing on the observability, detection, investigation, incident response, and intelligence portions of the security lifecycle. You will collaborate closely with preventative security engineering teams and other cross-functional teams.
Responsibilities:- Manage and grow a high-performing D&R team, planning strategy and hiring to support Anthropic's rapid growth and unique AI safety requirements
- Navigate prioritization in a fast-paced frontier environment, balancing operational demands with building innovative, scalable solutions for the future
- Collaborate across security engineering teams to build comprehensive prevention, observability, detection, and response capabilities throughout the security lifecycle
- Facilitate development of scalable, AI-leveraged D&R solutions that enable self-service observability and detection capabilities across Anthropic
- Build partnerships with product, infrastructure, and research teams to instill security monitoring best practices
- Own and continuously improve Security Incident Response, Data Management, and Detection Engineering policies and playbooks
- Operate our threat intelligence program and maintain relationships with external security partners and information sharing communities
- Continuously drive capability maturity across the detection lifecycle, establishing metrics and KPIs to measure effectiveness
- 5+ years building detection and response capabilities in a cloud-native organization
- 5+ years of engineering management experience with a proven track record of building and scaling security teams
- Deep understanding of security monitoring, threat detection, incident response, and forensics best practices
- Experienced in securing complex cloud environments (Kubernetes, AWS/GCP) with modern detection technologies
- Knowledgeable in AI/ML security risks, detection patterns, and response strategies
- Strong verbal and written communication skills with the ability to distill complex security topics
- Skilled at collaborating cross-functionally and effectively balancing security requirements with business objectives
- Able to drive high-impact work while incorporating feedback and adapting to changing priorities
- Passionate about building diverse, high-performing teams and growing engineers in a fast-paced environment
- Low ego, high empathy, and have a track record as a talent magnet who attracts and retains top security talent
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
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.
How we're differentWe 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
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What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
