Anthropic AI Safety Fellow Interview Guide

TL;DR

Role focus: Anthropic Fellows Program, AI Safety; empirical AI safety research; alignment science; scalable oversight; adversarial robustness; AI control; model organisms; mechanistic interpretability; AI welfare

Anthropic’s AI Safety Fellowship is not a normal software engineering interview, and it is not a standard academic research interview. It sits somewhere between a research apprenticeship, a practical Python coding screen, an empirical ML evaluation, and a mission-fit interview. The program is designed to help promising technical people transition into AI safety research by giving them funding, compute, mentorship from Anthropic researchers, and a structured full-time research environment.

According to Anthropic Careers — Fellows Program, Fellows primarily use external infrastructure such as open-source models and public APIs to work on empirical projects aligned with Anthropic’s research priorities, with the goal of producing public outputs such as paper submissions. The current broader Fellows Program includes AI Safety, AI Security, ML Systems & Performance, Reinforcement Learning, and Economics workstreams. (Greenhouse)

Note The key signal is not “I already have a perfect AI safety publication record.” According to Anthropic Alignment Science Blog — Anthropic Fellows Program 2026, Anthropic cares much more about research execution than credentials. Strong candidates can code well in Python, make progress on ambiguous problems, think clearly about hard technical questions, learn quickly, debug when things break, and explain why they want to reduce catastrophic risks from advanced AI systems. (Anthropic)

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What Is the Anthropic AI Safety Fellowship?

The Anthropic AI Safety Fellowship is a full-time, fixed-duration research program for people who want to work on empirical AI safety. According to Anthropic Careers — Fellows Program, the current program offers 4 months of full-time research, direct mentorship from Anthropic researchers, access to a shared workspace in Berkeley or London, connection to the broader AI safety and security research community, a weekly stipend, and compute funding of roughly $15,000/month. (Greenhouse)

The fellowship is aimed at people who may not yet be established AI safety researchers but have enough technical ability and research taste to produce useful work quickly. According to Anthropic Alignment Science Blog — Anthropic Fellows Program 2026, fellows work on empirical research questions aligned with Anthropic’s research priorities, and the expected output is often public research such as a paper. (Anthropic)

Anthropic reports that in its first cohort, over 80% of fellows produced papers and over 40% subsequently joined Anthropic full-time. Treat those numbers as historical outcome signals, not a guarantee for future cohorts. (Anthropic)

Note Think of the fellowship as a research execution test. Anthropic is trying to identify people who can become productive empirical safety researchers quickly, not people who merely know AI safety vocabulary.

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Interview Process

The current official process is described at a high level. According to Anthropic Careers — Fellows Program, the interview process includes an initial application and reference check, technical assessments and interviews, and a research discussion. (Greenhouse)

Older Anthropic AI Safety Fellow postings were more detailed. According to an archived Anthropic AI Safety Fellow posting on the Lionheart Ventures Job Board, the 2025 process included an initial application and references, a 90-minute Python technical assessment, a 55-minute coding-based technical interview without machine learning, a culture interview, a 15-minute research discussion, a 5-hour take-home project with a 30-minute review, reference calls, and offer decisions. That older process is useful historical signal, but your current recruiter instructions should be treated as the source of truth. (Lionheart Ventures)

A practical process approximation looks like this:

1. Application and references You submit your background, research interests, workstream preferences, and references. The current posting says the process begins with an application and reference check. (Greenhouse)

2. Technical assessment Expect a Python-heavy implementation assessment. The older posting described this as a 90-minute coding screen involving refactoring and adapting to new requirements rather than artificial algorithm puzzles. (Lionheart Ventures)

3. Technical interview A live coding interview may test practical implementation skill, debugging, data structures, and communication. The older process described this round as a 55-minute coding interview that did not involve machine learning. (Lionheart Ventures)

4. Research discussion Expect a short, high-density research brainstorming conversation. In the older process, this was a 15-minute research discussion designed to explore research ideas and approaches. (Lionheart Ventures)

5. Research-focused take-home or notebook exercise Some versions of the process may include a research-focused take-home. The older process listed a 5-hour take-home project plus a 30-minute review, focused on technical and analytical ability. (Lionheart Ventures)

6. Reference calls and offer decision References may be contacted during the process. In the older process, reference calls happened in parallel with final interviews; in the current posting, reference checking is listed as part of the initial process. (Greenhouse)

Note Read every email carefully. This process has changed across cohorts. The current public posting is high-level, while older postings include more specific steps. Your actual instructions from Anthropic or its recruiting partner should override any public guide.

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Application and References

The application is more than a resume screen. Anthropic wants to know whether you can become productive in empirical AI safety, whether your motivation is genuine, and whether people who have worked with you can vouch for your independence, technical ability, and collaboration.

According to Anthropic Careers — Fellows Program, candidates across workstreams may be a good fit if they are motivated by making AI safe and beneficial, excited to transition into empirical AI research, have a strong technical background in computer science, mathematics, or physics, thrive in fast-paced collaborative environments, and can implement ideas quickly and communicate clearly. Candidates must be fluent in Python and available to work full-time on the program. (Greenhouse)

Application Questions

• Why do you want to work on AI safety?
• Why Anthropic?
• Why the Fellows Program rather than a PhD, research engineering role, or independent research?
• What technical work are you most proud of?
• Describe a time you made progress on an ambiguous technical problem.
• What AI safety research direction are you most interested in?
• What is your strongest technical skill?
• What is a research question you would be excited to investigate?
• What evidence do you have that you can execute independently?
• Which workstream is your strongest fit, and why?
• Who are your references, and what can they say about your work?

How to Stand Out

A weak application says:

“I am interested in AI safety because AI is powerful and could be dangerous.”

A stronger application says:

“I want to work on empirical AI safety because I’m most convinced by research programs that produce measurable evidence about model behavior. My strongest fit is model organisms or AI control: I’ve built evaluation pipelines, debugged model behavior under distribution shift, and I’m interested in designing controlled experiments that reveal when models behave deceptively or strategically under pressure.”

The stronger answer connects motivation, technical background, research taste, and execution ability.

Note References matter because the fellowship is short and intense. Anthropic needs confidence that you can move quickly, handle ambiguity, receive feedback, and collaborate with mentors without requiring the structure of a normal school or job environment.

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Technical Assessment

The technical assessment is likely to be implementation-heavy. Based on the older Anthropic AI Safety Fellow posting, the coding screen was a 90-minute Python assessment involving refactoring and adapting to new requirements, without highly artificial scenarios or memorized algorithm tricks. (Lionheart Ventures)

That matters because many candidates prepare for the wrong thing. This is unlikely to be a classic “invert a binary tree” interview. It is more likely to test whether you can read requirements, model state, write clean Python, add features incrementally, and maintain correctness as the problem changes.

Technical Assessment Questions

• Implement an in-memory key-value store.
• Add TTL support to an in-memory database.
• Implement range queries or prefix queries over stored records.
• Refactor a working solution to support a new requirement.
• Process a stream of events and maintain aggregate state.
• Implement a simple job queue with priorities.
• Build a cache with expiration and update semantics.
• Write a parser for structured logs and support multiple query types.
• Implement a small experiment tracker with runs, metrics, and filters.
• Build an evaluator that scores model outputs against labels or rubrics.

What They Are Really Testing

The assessment is testing practical coding, not LeetCode memorization. Strong candidates can:

• Read a dense prompt quickly.
• Identify the required state representation.
• Write clean Python under time pressure.
• Build incrementally.
• Pass tests before adding complexity.
• Avoid over-engineering.
• Handle edge cases.
• Refactor without breaking earlier behavior.
• Debug quickly when tests fail.

A strong approach sounds like this:

“I’ll first implement the minimal data model that passes level one. I’ll keep the interface small so I can add TTL and versioning later. Before optimizing, I’ll make sure each operation has clear semantics around missing keys, expired values, and timestamp ordering.”

Note Practice coding with plain Python and tests. Do not rely too much on AI coding tools or IDE magic. According to Anthropic Candidate AI Guidance, Anthropic encourages candidates to use Claude for preparation and refinement, but using Claude during assessments is not allowed unless Anthropic explicitly permits it. (Anthropic)

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Technical Interview

The live technical interview may be similar to the technical assessment but with more emphasis on communication. The older Anthropic AI Safety Fellow posting described a 55-minute coding-based technical interview that did not involve machine learning. (Lionheart Ventures)

Technical Interview Questions

• Implement a streaming database with a given set of constraints.
• Extend an in-memory database to support TTL or historical queries.
• Implement a batch evaluator for model outputs.
• Build a small experiment logging system.
• Implement a queue that supports retries and failure states.
• Write a function to compare two model outputs under a scoring rubric.
• Build a small data transformation pipeline.
• Implement a scheduler that supports dependencies between jobs.
• Debug a function that processes generated model outputs.
• Refactor a slow implementation and explain the tradeoff.

Strong Answer Structure

A strong live coding answer usually follows this flow:

1. Restate the problem in your own words.
2. Ask clarifying questions.
3. Choose a simple data model.
4. Explain the interface before coding.
5. Write the smallest correct version first.
6. Add requirements incrementally.
7. Test edge cases verbally or with code.
8. Explain complexity and failure modes.
9. Discuss how the solution would change at larger scale.

A strong answer sounds like this:

“I’m going to treat timestamps as monotonically increasing unless you tell me otherwise. For the first version, I’ll store each key’s current value in a dictionary. For TTL, I’ll store the expiration timestamp alongside the value and centralize expiration checking in one helper so every read path behaves consistently.”

That kind of answer shows engineering discipline, not just coding speed.

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Research Discussion

The research discussion is a major differentiator. In the older Anthropic AI Safety Fellow process, the research discussion was a short brainstorming session intended to explore research ideas and approaches. (Lionheart Ventures)

This round is not a literature exam, but you should know Anthropic’s research directions well enough to propose concrete empirical projects. According to Anthropic Careers — Fellows Program, the AI Safety Fellows workstream includes scalable oversight, adversarial robustness and AI control, model organisms, model internals / mechanistic interpretability, and AI welfare. (Greenhouse)

Research Discussion Questions

• What AI safety problem are you most interested in, and why?
• Propose an empirical project in scalable oversight.
• How would you design a model organism of misalignment?
• How would you evaluate whether a model is strategically deceptive?
• How would you test whether chain-of-thought is faithful?
• How would you investigate whether a model’s persona affects its behavior?
• How would you design an AI control experiment?
• What would make an adversarial robustness benchmark realistic?
• How would you study whether activation monitors generalize out of distribution?
• What is a concrete safety experiment you could run in four months?
• What result would make you change your mind?
• How would you turn a vague safety concern into a measurable experiment?

Strong Answer Framework

Use this structure:

1. Research problem What safety-relevant question are you trying to answer?

2. Threat model or motivation Why does this matter for advanced AI systems?

3. Hypothesis What do you predict?

4. Experimental setup Which models, tasks, prompts, environments, datasets, or interventions would you use?

5. Metrics How would you measure the phenomenon?

6. Baselines and controls What comparison groups make the result interpretable?

7. Failure modes How could the experiment mislead you?

8. Expected outputs What would the paper, dataset, benchmark, code, or negative result look like?

9. Four-month scope What is the smallest useful version of the project?

A strong answer sounds like this:

“I’d study whether behavioral monitors fail exactly when they are most needed. I’d create actor-monitor setups where the actor is incentivized to hide harmful behavior, then compare trusted monitoring, untrusted monitoring, paraphrasing before monitoring, and activation-based anomaly detection. The key metric would not just be average detection accuracy; it would be false-negative rate on adversarially selected episodes.”

That answer shows research taste: it connects a safety problem, empirical setup, metric, and failure mode.

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Research-Focused Take-Home or Notebook Exercise

Some versions of the process may include a take-home project or notebook exercise. The older Anthropic AI Safety Fellow posting described a 5-hour work period plus a 30-minute review for a research-focused project designed to demonstrate technical and analytical abilities. (Lionheart Ventures)