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Anthropic Machine Learning Engineer Interview Questions

Review this list of 34 Anthropic Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • "Functional requirements: user can send an input and wait for the result Group up to 100 individual requests in to single GPU The system should should send results back to the user who requested it when done Non functional requirements: Minimize the waiting between two batches of execution/ reduce idle time error message if a batch faiils Scale to support multiple GPUs Core Entities: Request Batch Result API Design: POST /predict -> {requestid: "", response: ""} req"

    Alok S. - "Functional requirements: user can send an input and wait for the result Group up to 100 individual requests in to single GPU The system should should send results back to the user who requested it when done Non functional requirements: Minimize the waiting between two batches of execution/ reduce idle time error message if a batch faiils Scale to support multiple GPUs Core Entities: Request Batch Result API Design: POST /predict -> {requestid: "", response: ""} req"See full answer

    Machine Learning Engineer
    Artificial Intelligence
    +5 more
  • Anthropic logoAsked at Anthropic 
    126 answers
    Video answer for 'Tell me about yourself.'
    +118

    "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"

    Bipin R. - "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"See full answer

    Machine Learning Engineer
    Behavioral
    +17 more
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    Machine Learning Engineer
    Coding
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  • Anthropic logoAsked at Anthropic 
    1 answer

    "I look at AI safety, ethics, guardrails, and governance as layers of one core responsibility: making systems useful without letting them cause harm. The Technical Core: Safety Safety is about managing technical risks like reliability, bias, and unintended behavior. It cannot be a final checklist before launch. It has to be part of the whole cycle, from how data is collected to how the system is monitored after it goes live. The Human Element: Ethics Ethics asks if a system should"

    Mark G. - "I look at AI safety, ethics, guardrails, and governance as layers of one core responsibility: making systems useful without letting them cause harm. The Technical Core: Safety Safety is about managing technical risks like reliability, bias, and unintended behavior. It cannot be a final checklist before launch. It has to be part of the whole cycle, from how data is collected to how the system is monitored after it goes live. The Human Element: Ethics Ethics asks if a system should"See full answer

    Machine Learning Engineer
    Artificial Intelligence
    +1 more
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Coding
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  • Anthropic logoAsked at Anthropic 
    70 answers
    +60

    "I follow a variation of the RICE framework when prioritizing how I ship product features. I start by looking at: Reach: Because the customer segmentation across our product portfolio is so similar, I tend to hold a lot of weight on product features that will maximize our customer reach with a minimal LOE. Impact: After establishing which customer segments will benefit from the product feature, I determine the urgency and estimated impact on each customer segment based on customer i"

    Ashley C. - "I follow a variation of the RICE framework when prioritizing how I ship product features. I start by looking at: Reach: Because the customer segmentation across our product portfolio is so similar, I tend to hold a lot of weight on product features that will maximize our customer reach with a minimal LOE. Impact: After establishing which customer segments will benefit from the product feature, I determine the urgency and estimated impact on each customer segment based on customer i"See full answer

    Machine Learning Engineer
    Behavioral
    +10 more
  • Anthropic logoAsked at Anthropic 
    31 answers
    +26

    "We can use dictionary to store cache items so that our read / write operations will be O(1). Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1) Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"

    Alfred O. - "We can use dictionary to store cache items so that our read / write operations will be O(1). Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1) Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +6 more
  • Anthropic logoAsked at Anthropic 
    2 answers

    "There are many good answers to this that AI scientists around the world, I and my coworkers have tried over the years. For one, RAG is a great option to fact-check and enforce citation generation, update the data in the knowledge base of the generative AI, etc. "

    Nathan B. - "There are many good answers to this that AI scientists around the world, I and my coworkers have tried over the years. For one, RAG is a great option to fact-check and enforce citation generation, update the data in the knowledge base of the generative AI, etc. "See full answer

    Machine Learning Engineer
    Artificial Intelligence
    +4 more
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    Machine Learning Engineer
    Artificial Intelligence
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  • Anthropic logoAsked at Anthropic 
    11 answers
    +8

    "In my time at Snapp! I was in charge of communicating the product backlog to our CEO. We had a shared Jira board that he had access to and I made specifically for him. One day he saw me in the office and said he doesn’t know anything about our backlog and that’s because I failed to communicate with him. I got upset at first because of the fact that I made the dashboard exclusively for him. But I tried to ask questions to understand his point of view in depth. He then mentioned he doesn't have t"

    Ra R. - "In my time at Snapp! I was in charge of communicating the product backlog to our CEO. We had a shared Jira board that he had access to and I made specifically for him. One day he saw me in the office and said he doesn’t know anything about our backlog and that’s because I failed to communicate with him. I got upset at first because of the fact that I made the dashboard exclusively for him. But I tried to ask questions to understand his point of view in depth. He then mentioned he doesn't have t"See full answer

    Machine Learning Engineer
    Behavioral
    +9 more
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    Machine Learning Engineer
    Behavioral
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    Machine Learning Engineer
    Artificial Intelligence
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  • Anthropic logoAsked at Anthropic 
    4 answers
    +1

    "Situation - A time I dealt with conflict while on a team was while I was working at Shopify on physical and digital gift card refund point of sale solutions. The situation was that we were dealing with complex technical constraints including not changing particular UI components behavior to act as they should be intended. On the refund screen, the existing design was using a toggle on the same screen to bring up a modal for gift card selection to either select digital or physical options. Thi"

    Ben G. - "Situation - A time I dealt with conflict while on a team was while I was working at Shopify on physical and digital gift card refund point of sale solutions. The situation was that we were dealing with complex technical constraints including not changing particular UI components behavior to act as they should be intended. On the refund screen, the existing design was using a toggle on the same screen to bring up a modal for gift card selection to either select digital or physical options. Thi"See full answer

    Machine Learning Engineer
    Behavioral
    +5 more
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Artificial Intelligence
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  • Anthropic logoAsked at Anthropic 
    4 answers
    +1

    "Hallucinations are evaluated by measuring how often generated outputs contain information that is not supported by trusted sources. what hallucination means in context: Intrinsic hallucination: contradicts provided context Extrinsic hallucination: introduces unsupported facts Fabrication: confidently incorrect answers"

    Hardik saurabh G. - "Hallucinations are evaluated by measuring how often generated outputs contain information that is not supported by trusted sources. what hallucination means in context: Intrinsic hallucination: contradicts provided context Extrinsic hallucination: introduces unsupported facts Fabrication: confidently incorrect answers"See full answer

    Machine Learning Engineer
    Artificial Intelligence
    +4 more
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    Machine Learning Engineer
    Artificial Intelligence
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    Machine Learning Engineer
    System Design
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    Machine Learning Engineer
    Artificial Intelligence
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    Machine Learning Engineer
    Artificial Intelligence
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    Machine Learning Engineer
    Analytical
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Showing 1-20 of 34