Landing an interview opportunity at Anthropic is exciting, but receiving an online assessment invitation can leave you with plenty of questions. What kind of coding challenges should you expect? How difficult is the test? And what is the best way to prepare?
The Anthropic OA (Online Assessment) is a technical evaluation used during recruitment for certain engineering and research-related positions. Depending on the role, candidates may encounter practical programming tasks, progressive coding challenges, or exercises involving existing code.
Unlike traditional coding interviews that focus primarily on algorithms, some reported Anthropic assessments place significant emphasis on building functional software and adapting it to changing requirements.
This guide explains the Anthropic OA format, common coding topics, effective preparation strategies, and mistakes to avoid. Whether you’re an experienced developer or preparing for your first technical assessment, understanding what to expect can help you approach the process with greater confidence.
What Is the Anthropic OA?
The Anthropic OA is an online technical assessment used to evaluate candidates applying for certain positions at Anthropic, the artificial intelligence company behind Claude.
Anthropic develops advanced AI systems with a focus on safety, reliability, and responsible deployment. Its technical teams work across software engineering, machine learning, research, and infrastructure.
Consequently, technical evaluations may assess more than a candidate’s ability to solve isolated programming problems.
What Skills Does the Assessment Evaluate?
Depending on the position and assessment format, candidates may be evaluated on several technical abilities:
- Programming proficiency: Writing functional, readable code in a supported programming language.
- Problem-solving: Breaking complex requirements into manageable implementation steps.
- Data structures: Choosing appropriate ways to store, retrieve, and manipulate information.
- Debugging: Identifying errors and correcting unexpected behaviour.
- Code organisation: Creating functions and structures that support additional features.
- Time management: Completing tasks within the assessment deadline.
For example, a candidate might be asked to build a simple banking application and then extend its functionality to support transfers, transaction histories, and account management.
Success requires understanding how different features interact rather than simply writing individual functions.
Who Takes the Anthropic Online Assessment?
Anthropic may use technical assessments for software engineering, research engineering, and other technical opportunities.
However, assessment requirements can differ significantly between roles.
Some software engineering candidates have described progressive coding exercises, while certain research fellowship applicants have reported working with existing codebases.
There is no publicly confirmed universal Anthropic OA format covering every position. Always treat your assessment invitation as the most reliable source of information.
Anthropic OA Format: What to Expect in 2026
Understanding the assessment structure can make preparation more focused and reduce unnecessary surprises.
Candidate reports have described timed CodeSignal assessments involving several connected programming tasks. Some applicants have reported a 90-minute duration, although this should not be treated as a standard time limit for every Anthropic position.
Understanding Progressive Coding Challenges
A progressive coding challenge begins with a relatively simple requirement and introduces increasingly complex features.
Consider a digital wallet system.
Stage 1: Basic Account Operations
Implement functions to create wallets and retrieve account balances.
Stage 2: Transaction Management
Add deposits, withdrawals, and balance validation.
Stage 3: Account Transfers
Allow users to transfer funds between wallets while preventing invalid transactions.
Stage 4: Advanced Features
Introduce transaction histories, account merging, or historical balance calculations.
This is an illustrative practice exercise, not a verified Anthropic assessment question.
The difficulty comes from maintaining existing functionality while implementing new requirements.
A poorly structured solution might work during the first stage but become difficult to extend later.
Therefore, clear code organisation and appropriate data structures are essential preparation priorities.
Is the Anthropic OA Similar to LeetCode?
Anthropic’s reported implementation-focused assessments are not necessarily identical to conventional LeetCode exercises.
LeetCode questions often emphasise algorithms, computational complexity, and efficient solutions to individual problems.
Progressive coding assessments may additionally test practical software development skills, including maintaining application state, modifying existing functionality, and handling complex requirements.
That doesn’t mean algorithm preparation is unnecessary.
It means candidates should balance algorithm practice with realistic software implementation exercises.
Common Anthropic OA Coding Questions and Topics
Anthropic does not publish a comprehensive official collection of current assessment questions.
However, candidate experiences suggest several areas that are useful for preparation.
1. In-Memory System Design
In-memory systems store and manage information while a program is running.
Practice problems might involve developing:
- A banking system supporting deposits and transfers
- A key-value database with search functionality
- A file storage application
- A task management platform
- A digital inventory system
For example, imagine implementing an inventory system for an electronics retailer.
Initially, the application only needs to store product names and quantities.
Later, it must support stock updates, product searches, transaction records, and ranked inventory reports.
The challenge is designing the initial structure so these additional features can be implemented without unnecessary rewrites.
2. Data Structures and Algorithms
Strong programming fundamentals remain valuable.
Candidates should be comfortable working with hash maps, arrays, lists, sets, sorting algorithms, and basic searching techniques.
Understanding when to use each structure is more important than memorising definitions.
For example, a hash map can support efficient account lookup, while sorting may be needed to generate reports based on transaction activity.
Practice selecting structures according to the problem rather than automatically choosing whatever feels familiar.
3. Reading and Modifying Existing Code
Some technical assessments may require candidates to understand an existing project and implement additional functionality.
This introduces challenges that are rarely encountered in isolated algorithm exercises.
You might need to identify where a particular feature is implemented, understand existing interfaces, and make changes without breaking unrelated functionality.
A practical preparation technique is to select a small open-source project and implement a new feature.
Begin by understanding the project structure before making modifications.
4. Edge Cases and Error Handling
Correct code must handle more than the most obvious inputs.
For a banking application, potential edge cases include:
- Attempting to withdraw more than the available balance
- Transferring money to an unknown account
- Creating duplicate account identifiers
- Processing invalid transaction amounts
- Requesting information about a deleted account
Testing these scenarios can reveal weaknesses that aren’t obvious during initial development.
Develop the habit of checking both normal operations and unexpected inputs.
How to Prepare for the Anthropic OA Effectively
Preparing for the Anthropic OA requires a combination of programming fundamentals, practical implementation, and realistic time management.
Solving hundreds of unrelated coding questions isn’t necessarily the most efficient approach.
Instead, practise building complete systems and extending them through several development stages.
Build Projects With Changing Requirements
Start with a simple application and gradually introduce new functionality.
For example, create a library management system.
First, implement book registration and removal. Next, add borrowing and returning features. Finally, include reservation queues, borrowing histories, and search functionality.
As requirements become more complex, evaluate whether your original design remains suitable.
This exercise develops the ability to adapt code without repeatedly starting from scratch.
Follow a Seven-Day Preparation Plan
If your assessment is approaching, a structured schedule can help you cover essential skills.
| Day | Preparation Activity |
|---|---|
| Day 1 | Review hash maps, lists, sorting, and fundamental algorithms |
| Day 2 | Build a basic in-memory database |
| Day 3 | Add advanced features and edge-case handling |
| Day 4 | Practise understanding and modifying unfamiliar code |
| Day 5 | Complete a timed progressive coding challenge |
| Day 6 | Review weaknesses and repeat difficult exercises |
| Day 7 | Complete a realistic mock assessment |
This schedule is a suggested preparation strategy, not an official Anthropic training plan.
Adjust the activities according to your experience and the requirements stated in your invitation.
Practise Under Realistic Time Limits
Time pressure can make straightforward programming problems unexpectedly difficult.
During preparation, complete mock assessments without constantly consulting tutorials or completed solutions.
If your invitation specifies a 90-minute assessment, practise within that limit.
After each session, review where time was lost.
Did you misunderstand the requirements? Did debugging take too long? Were your data structures difficult to extend?
Identifying recurring problems allows you to make more meaningful improvements.
Use AI Tools Responsibly
AI assistants can be helpful during preparation.
You can use tools such as Claude to generate practice exercises, explain unfamiliar programming concepts, or review code quality.
However, preparation and assessment participation are different situations.
Anthropic’s published candidate guidance states that AI tools should not be used during take-home assessments or live interviews unless explicitly permitted.
Always follow the rules supplied with your particular assessment.
Common Mistakes to Avoid During the Anthropic OA
Technical knowledge alone doesn’t guarantee strong assessment performance.
Several avoidable mistakes can make otherwise manageable challenges more difficult.
Overcomplicating the Initial Solution
Some candidates spend too much time designing complex architectures before implementing basic functionality.
This can create unnecessary work and leave less time for later requirements.
Start with a straightforward solution that meets the specification.
Use clear function names, consistent data structures, and simple logic.
Introduce additional complexity only when the requirements justify it.
Ignoring Previously Completed Features
Progressive assessments may require later features to work alongside earlier functionality.
A change that solves the newest task but breaks an existing operation can cause multiple test failures.
After making significant modifications, rerun relevant tests.
When errors appear, identify the underlying cause rather than rewriting large sections without a clear reason.
Spending Too Much Time on One Requirement
A difficult feature can consume a large portion of the assessment.
Prioritise implementing essential functionality correctly before attempting optional improvements.
For a practice assessment, one possible time allocation is:
- 10% for reading and understanding requirements
- 70% for implementation
- 20% for testing and debugging
This is a general time-management guideline, not an official Anthropic scoring strategy.
Assuming a High Score Guarantees an Interview
A strong coding assessment score does not automatically guarantee progression.
Recruitment decisions may consider professional experience, role alignment, technical qualifications, and other evaluation stages.
In a September 2026 candidate discussion, one applicant reported receiving a high CodeSignal score but still being rejected before an interview.
Individual reports cannot establish the company’s complete selection criteria.
Focus on delivering strong technical performance while recognising that the hiring process extends beyond a single assessment.
What Happens After the Anthropic OA?
Candidates who progress beyond the online assessment may be invited to additional interviews.
The exact process depends on the position.
Subsequent stages may include live coding, technical discussions, system design exercises, or conversations about previous projects.
Prepare to Explain Your Code
During technical interviews, interviewers may ask you to explain your implementation decisions.
Be ready to discuss:
- Why you selected a particular data structure
- How your solution handles unusual inputs
- What limitations exist in your implementation
- How you would improve performance or maintainability
- What alternative approaches you considered
Clear technical communication is an important professional skill.
Practise explaining your reasoning in plain language rather than relying on memorised answers.
Research Anthropic Before Your Interview
Understanding Anthropic’s work can help you connect your skills to the position.
Explore the company’s research into AI safety, its Claude products, and the engineering challenges associated with developing AI systems.
Review the job description carefully.
Identify examples from your previous projects that demonstrate relevant skills and responsibilities.
Specific experience is generally more useful in an interview than broad statements about being interested in artificial intelligence.
Frequently Asked Questions About the Anthropic OA
Is the Anthropic OA difficult?
The Anthropic OA can be challenging, particularly for candidates unfamiliar with progressive coding tasks or unfamiliar codebases. Difficulty depends on the position and assessment format. Practising realistic programming exercises and debugging techniques can help improve preparation.
How long is the Anthropic online assessment?
Some candidates have reported 90-minute CodeSignal assessments for software engineering positions. However, there is no publicly confirmed universal duration for every Anthropic OA. Check your invitation for the specific time limit.
Can I use Python for the Anthropic OA?
Python may be available in certain technical assessments, but supported languages vary according to the platform and test. Confirm the available programming languages in your assessment instructions before beginning.
Can I use ChatGPT or Claude during the Anthropic OA?
You should not use ChatGPT, Claude, or other AI assistants during an assessment unless the instructions explicitly allow them. Anthropic encourages responsible AI use for preparation but places restrictions on AI assistance during evaluations.
What is the passing score for the Anthropic OA?
There is no publicly verified universal passing score for all Anthropic online assessments. Requirements may vary by position, and recruitment decisions can involve factors beyond coding performance.
Conclusion
The Anthropic OA can test much more than your ability to solve individual coding problems. Depending on the position, candidates may need to develop working systems, interpret unfamiliar code, implement changing requirements, and demonstrate effective debugging skills.
The most productive preparation combines programming fundamentals with realistic implementation exercises.
Start with a small in-memory application, introduce additional requirements, and test each feature carefully. Complete timed practice sessions to identify weaknesses before your assessment.
Most importantly, avoid relying entirely on memorised solutions or unverified interview questions.
Review the instructions in your assessment invitation, consult Anthropic’s official careers resources, and focus on building reliable problem-solving skills.
Recommended next step: Complete one progressive coding mock assessment and evaluate your implementation, testing, and time management before moving on to more advanced challenges.
