Okay, I've searched for Research Engineer roles in San Francisco, CA, specializing in AI safety and evaluation infrastructure, requiring 5+ years of experience, full-time positions, with Python and distributed systems skills. I have found 152 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Research Engineer role within Anthropic’s Frontier Red Team is fundamentally about designing and implementing advanced evaluation systems that ensure the safety and reliability of cutting-edge AI models before deployment. This position sits at the nexus of AI safety, engineering, and interdisciplinary collaboration, requiring the candidate to build scalable infrastructure to test for catastrophic risks like biosecurity threats, autonomous replication, and cybersecurity breaches. The role demands a builder’s mindset—rapidly prototyping, iterating, and scaling novel evaluation frameworks under pressure to maintain pace with fast-moving frontier AI technologies. Success hinges on strong software engineering skills, particularly in Python and distributed systems, combined with a genuine passion for AI safety and responsible development. While domain expertise in risk-specific areas is a bonus, the core challenge is one of converting abstract risk concepts into concrete, automatable tests, often with incomplete precedents. The candidate must exercise sound judgment balancing urgency with methodological rigor, continuously collaborating with domain experts and cross-functional teams to deliver robust evaluation results that directly inform critical, high-stakes company decisions. Over the first 6 to 12 months, success would likely be defined by the ability to rapidly build and deploy scalable evaluation pipelines, contribute to industry-leading safety standards, and effectively communicate results that shape frontier model deployment policies.

Company Analysis:

Anthropic is positioned as a leading, mission-driven AI research organization focused on creating safe, interpretable, and steerable AI systems with broad societal benefit. Rather than pursuing scattered smaller projects, Anthropic emphasizes large-scale, high-impact research that advances core safety and trustworthiness goals, reflecting a scientific rigor more akin to physics or biology. This focus on responsible scaling aligns tightly with this role’s mandate to develop industry-standard evaluation frameworks for frontier models. The company culture appears highly collaborative, interdisciplinary, and mission-oriented—valuing communication, diversity of perspectives, and ethical responsibility. For a candidate, thriving here means embracing a fast-paced, experimental environment that prizes both rapid execution and thoughtful implementation, within a supportive team committed to high standards and transparency. Organizationally, the role reports into a high-visibility, cross-team function that directly informs top leadership decisions on model deployment, placing it in a strategic position to influence company direction and industry safety norms. Given Anthropic’s status as a public benefit corporation with a research-first mindset, this role represents a strategic hire critical to scaling their safety infrastructure as they continue pioneering advanced AI capabilities with global implications.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time you led a fast-paced engineering project with uncertain outcomes. How did you prioritize rapid iteration while maintaining quality?
• Behavioral: Describe an experience where you collaborated with domain experts outside your immediate technical specialty. How did you ensure shared understanding and effective outcomes?
• Behavioral: Have you ever identified a significant risk or flaw during a system evaluation that led to a change in deployment decisions? What was your approach and how did you communicate it?
• Behavioral: Share an example of balancing urgency with methodical implementation in a safety- or security-critical project.
• Technical: How would you design an evaluation framework to measure emerging, previously unseen capabilities in a large language model?
• Technical: Explain your approach to building scalable testing infrastructure that can automate continuous model assessments across diverse risk domains.
• Technical: What are the critical considerations when developing sandboxed environments for safely testing AI models capable of autonomous or potentially harmful actions?
• Technical: Discuss your experience with distributed systems in the context of high-throughput evaluation pipelines. How do you ensure reliability and scalability?
• Situational: Imagine you discover a newly surfaced AI capability that could be exploited maliciously but lacks existing evaluation methods. How would you proceed to assess and contain this risk?
• Situational: You receive conflicting feedback from cybersecurity and biosecurity experts about an evaluation's design. How would you mediate and align the evaluation approach?
• Situational: If a model crosses an autonomous security threshold during evaluation, but deployment pressure mounts, how would you contribute to the company’s decision-making process?
• Company Fit: Why does Anthropic’s mission to build steerable and interpretable AI systems resonate with you, and how does it influence your approach to engineering safety evaluations?
• Company Fit: Anthropic emphasizes collaboration between researchers, engineers, and policy experts. How do you see your role contributing to this interdisciplinary culture?
• Candidate Questions: Could you describe how the Frontier Red Team collaborates with product and policy teams to influence deployment decisions?
• Candidate Questions: What are the biggest technical challenges your team currently faces in scaling evaluation systems for frontier models?
• Candidate Questions: How does Anthropic support continuous learning and professional growth for engineers working on evolving AI safety challenges?