Anthropic Fellows Program: Application Guide & Strategies

1. ML Systems & Performance Fellows — large-scale AI infrastructure, distributed systems, performance optimization.
2. Reinforcement Learning Fellows — RL systems, training environments, evaluation methods.
3. AI Security Fellows — AI-enabled cyber risks, vulnerability analysis, adversarial systems.

1. AI Safety Fellows — reducing catastrophic risks from advanced AI systems. Looking for curious, motivated people regardless of credentials.
2. Economics & Societal Impacts Fellows — AI's effects on labor markets, economic systems, governance, and policy. Hosted via The Anthropic Institute.

1. Pick your track honestly. ML Systems, RL, and AI Security need real engineering depth. AI Safety and Economics are open to strong generalists — don't force yourself into a technical track if it's not your background.
2. Nail the "why this track" answer. Every application asks you to articulate a specific research area you're excited about and tie it to a track. Vague answers ("I love AI") get filtered immediately.
3. Line up 3 strong references now. Anthropic prefers people from the ML/research community who can speak concretely to your strengths AND weaknesses. Brief them on the fellowship and your application before you submit — Anthropic may call them at any point, including last-minute during the final round.
4. Use Claude to polish (not write) your application. Draft your motivation/research-interest answers yourself first, then use Claude to tighten clarity and cut fluff. AI-generated-sounding applications are an easy filter-out.
5. Apply early — it's rolling. Cohorts start multiple times a year; earlier applications get more runway through a multi-week loop.
6. Don't self-select out. Anthropic explicitly says they care more about your ability to execute on research than your credentials or pedigree.

The core skill being tested is NOT algorithms — it's building small systems incrementally under time pressure.

• Practice "build a system, then extend it" problems, not LeetCode-style puzzles. Examples from real candidates:
• Design for extension from the start. Stage 1 code that's too rigid will force a rewrite by stage 3. Practice writing clean, modular code even under time pressure — think about what "stage 2" might ask before you've been told.
• Time-box ruthlessly. The OA is 90 minutes across 4 stages (250 pts each). If you're stuck on stage 3, get a partial solution working and move on — don't lose stage 4 entirely chasing perfection on stage 3.
• For RL specifically: be comfortable with training loops, reward functions, evaluation harnesses, and basic RL algorithms (policy gradients, PPO basics) — even at a conceptual/implementation level, not just theory.
• For AI Security: practice thinking like an attacker — vulnerability analysis, adversarial inputs, red-teaming mindset. Be ready to discuss how you'd probe a system for weaknesses, not just how to defend it.
• For ML Systems: brush up on distributed systems fundamentals — concurrency, sharding, caching, performance bottlenecks. Practice profiling and optimizing code, not just writing it.

• The prompt will be dense — that's intentional. Real candidates report 10–15 minutes of clarifying questions is expected before writing code. Practice reading a spec, then forcing yourself to ask 3–5 clarifying questions before touching the keyboard.
• Narrate your thinking, but stay efficient. Anthropic engineers are described as direct and time-pressed — skip small talk, narrate only what moves the problem forward.
• Expect a mid-round twist. The format is "main task + follow-up extension." Practice adapting working code to a new requirement on the fly without starting over.
• Mock interview this specifically. Find a partner/coach, give them a deliberately dense prompt, and have them interrupt with a follow-up extension halfway through.

• Test your environment in advance. This round runs in a Google Colab notebook with GPU access — confirm GPU execution before the interview so you're not burning interview time on setup.
• Practice completing partial LLM pipeline code. Be comfortable reading 20-30 lines of skeleton code involving model inference/output processing and filling in missing pieces under time pressure.
• Prioritize the implementation task over the discussion. Most candidates only finish Part 1 (the coding task) in the 55 minutes — treat the prompt-engineering discussion as a bonus you earn by moving fast through Part 1.
• Be ready to discuss, out loud:
• Your AI safety framing matters here. Interviewers are explicitly looking for whether your design choices reflect genuine safety consciousness, not just technical optimization.

• This round has no right answer and no feedback signal — interviewers won't tell you if you're "on track." Practice committing to a direction and defending it without external validation.
• Read Anthropic's actual published research before this round:
• Practice framing vague prompts into tractable questions, fast. You have ~15 minutes total to go from prompt → framing → at least one substantive idea.
• Sample prompts to rehearse against (real examples from candidates, alignment-focused):
• For non-alignment tracks, adapt this same drill to your track's domain (e.g., economics: "How would you measure AI's labor market displacement effects?").

I'm applying for the [TRACK NAME] Fellows track at Anthropic. 
Help me refine (not write from scratch) a statement of interest that:
- Connects my background in [YOUR BACKGROUND] to this specific track
- Names a specific research area I'm excited about and explains why
- Highlights relevant projects or experience (even if non-traditional)
- Demonstrates genuine motivation to work on AI safety/security/economics
- Sounds like me, not like an AI — keep my voice and phrasing
- Is concise (under 300 words)

Here's my first draft: [PASTE YOUR DRAFT]

Act as an Anthropic Fellows technical interviewer for the [ML Systems / 
Reinforcement Learning / AI Security] track.

Give me a "build a system, then extend it" coding prompt similar to what's 
used in Anthropic's CodeSignal assessment — start with a base system, 
then add 2-3 progressively harder extensions.

After I describe my approach, critique it for:
- Whether my initial design supports the later extensions without a rewrite
- Time management across stages
- Edge cases I'm missing
Be direct about weaknesses.

Act as a potential Anthropic mentor running a 15-minute research brainstorm 
for the AI Safety Fellows track. Give me an open-ended alignment or safety 
prompt (e.g. related to misalignment detection, robustness, or scalable 
oversight).

Don't give me feedback on whether I'm "on the right track" — just let me 
think out loud, then afterward, evaluate:
- How quickly I framed a tractable research question
- Whether my proposal shows real grounding in current alignment research
- How well I defended my reasoning without guidance