Anthropic, the AI research company behind the Claude language model series, has quickly become a leading voice in the safe and interpretable AI movement. Their mission is grounded in alignment and responsibility, and that philosophy carries into their hiring process. Anthropic OA for software engineering roles reflects their high technical standards. Candidates are evaluated not only on their algorithmic problem-solving ability, but also on writing clean, maintainable code that demonstrates systems-level thinking, privacy awareness, and performance trade-offs.
Below are three representative Anthropic-style OA questions, along with insights and code samples.
Question 1: Sensitive Data Redactor
Problem:
Write a function that scans a block of user-generated text and redacts sensitive entities such as email addresses, phone numbers, and credit card numbers by replacing them with [REDACTED].
Input:
text = "Contact me at [email protected] or call 555-123-4567. My Visa is 4111-1111-1111-1111."Expected Output:
"Contact me at [REDACTED] or call [REDACTED]. My Visa is [REDACTED]."Question 2: Token Budget Optimizer
Problem:
You are given a list of generated text segments from an LLM, each with a token count and a relevance score. Your goal is to return the most relevant subset of segments such that their combined token count does not exceed a given limit.
This is a variant of the 0/1 Knapsack Problem.
Input:
segments = [
{"text": "Answer A", "tokens": 5, "score": 8},
{"text": "Answer B", "tokens": 7, "score": 13},
{"text": "Answer C", "tokens": 4, "score": 6}
]
limit = 10
Expected Output:
A subset (e.g., ["Answer A", "Answer C"]) maximizing score under token limit.
Reference Code:
def token_budget_optimizer(segments, limit):
n = len(segments)
dp = [[0] * (limit + 1) for _ in range(n + 1)]
for i in range(1, n + 1):
tokens = segments[i-1]['tokens']
score = segments[i-1]['score']
for j in range(limit + 1):
if tokens <= j:
dp[i][j] = max(dp[i-1][j], dp[i-1][j-tokens] + score)
else:
dp[i][j] = dp[i-1][j]
# Reconstruct the chosen subset
res = []
j = limit
for i in range(n, 0, -1):
if dp[i][j] != dp[i-1][j]:
res.append(segments[i-1]['text'])
j -= segments[i-1]['tokens']
return res[::-1]
Question 3: Rate-Limited Logger
Problem:
Design a logging system that prints incoming messages only if the same message hasn’t been printed in the last 10 seconds. This simulates logging at scale while avoiding log flooding.
Input:
log_system.should_log(1, "error: out of memory") # True
log_system.should_log(3, "error: out of memory") # False
log_system.should_log(11, "error: out of memory") # True
Output:
Return True if the message should be printed; else False
FAQ: Anthropic Online Assessment
What is the Anthropic Online Assessment (OA)?
The Anthropic Online Assessment is a technical screening used to evaluate candidates’ real-world engineering ability. Unlike traditional algorithm-heavy tests, the Anthropic OA focuses on practical coding tasks, system-style problems, and clean, maintainable solutions that resemble production engineering work.
What roles require completing the Anthropic OA?
The Anthropic OA is commonly required for Software Engineer, Research Engineer, and other technical roles. The exact format may vary slightly depending on the team, but the core assessment style remains consistent across engineering positions.
What types of questions appear in the Anthropic OA?
Anthropic OA questions typically include:
- Practical coding problems (not pure LeetCode tricks)
- Stateful systems or data processing tasks
- String parsing, logging, rate-limiting, or optimization problems
- Scenarios related to safety, reliability, and performance
Expect problems that test how you think like an engineer, not just how fast you code.
How difficult is the Anthropic Online Assessment?
The Anthropic OA is considered medium to hard, but the difficulty comes from design thinking rather than advanced algorithms. Many candidates find it challenging because it rewards clarity, edge-case handling, and system reasoning instead of memorized patterns.
How long does the Anthropic OA take?
Most candidates report spending 60–90 minutes on the Anthropic Online Assessment. Time pressure is real, so efficiency, planning, and writing correct code early are critical.
Does Anthropic expect optimal algorithmic complexity?
Anthropic values reasonable performance, but readability and correctness matter more than extreme optimization. A clean O(n) or O(n log n) solution with good structure is usually preferred over a complex, hard-to-read implementation.
Can I use external libraries during the Anthropic OA?
This depends on the platform used for the OA. In most cases, standard language libraries are allowed, but external third-party packages are restricted. Always read the platform instructions carefully before starting.
How should I prepare for the Anthropic OA?
Effective Anthropic OA preparation includes:
- Practicing real-world coding problems instead of only LeetCode
- Building small systems (parsers, caches, loggers, schedulers)
- Writing clean, testable code under time constraints
- Getting comfortable explaining design trade-offs
Mock assessments that simulate real OA pressure are especially helpful.
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