Anthropic Machine Learning Engineer Interview Guide
Updated by Anthropic candidates
Written by Graham Carlson, Senior Technical ContributorAnthropic has distinguished itself with its deep research into AI ethics, compliance, and data governance.
Their interview process is known to be rigorous, and the round with the highest failure rate is the culture fit round.
Below, we break down the complete machine learning engineer interview process at Anthropic for junior and senior candidates.
Interview Process
Anthropic’s ML Engineer interview process can vary team to team, and here is a look at a typical process:
- Recruiter screen(s): 15-minute call(s) with a recruiter covering your background and interest in Anthropic.
- Technical screen: 60-minute assessment of technical skill requiring you to respond to an error message and propose a solution.
- Onsite loop: A set of five interviews, covering ML operations, behavioral fit, cultural fit, ML design, and a discussion with the hiring manager.
We created this guide with direct input from machine learning engineers at Anthropic. It reflects current interview practices and evaluation criteria used by Anthropic hiring teams.
Recruiter screen
This 15-minute call will cover the key aspects of the role as well as your direct experience with machine learning, AI tooling, and other relevant topics. Candidates have reported that, despite the limited time, recruiters tend to probe and ask deeper questions rather than just checking boxes.
Recruiter questions:
Technical phone screen
Anthropic favors practical questions that draw from the actual responsibilities of the role, and you will be allowed to use Claude Sonnet to work.
You will be granted access to their tech stack, along with an error message, and you will need to propose a solution that improves model reliability when handling long-running tasks. You might need to pay special attention to the MCP tooling and manage the context window that the model is drawing from.
Technical Phone Screen Questions:
Asked at Anthropic • Asked at Anthropic • Onsite loop
If you are located far from Anthropic’s offices, you may be allowed to conduct these rounds remotely. Each round is 60 minutes long.
ML operations
This round will require you to analyze and improve the code of one of Anthropic’s tools, testing your ability to wring better efficiency and reliability while still managing the size of the context window.
You might be given existing code related to their agent feedback loops and asked to suggest ways to improve performance. You’ll need to assess everything from performance tuning and memory management to output assessment and consistency.
ML Operations Questions:
Asked at Anthropic • Hiring manager interview
Likely topics include:
- Your experience working at high scale
- Past failures
- Cross functional experience
- Questions gauging your emotional intelligence
- How you use AI in your workflow
- How AI relates to ML
- How do you get AI models production-ready?
- Your experience managing data governance rules, building system guardrails
- Your ability to balance data security with system functionality and performance
Hiring Manager Interview Questions:
Asked at Culture fit
This interview often involves difficult or probing questions, particularly ones focused on failures, ethical concerns, or executive pressure. Being forthright about past and current challenges will show your ability to self-assess and highlight your emotional intelligence.
They will also ask you about handling criticism from others on your team and your ability to step outside your narrow responsibilities to understand others with different skill sets or backgrounds.
The key to this round is being honest and open, which means admitting to failure or discomfort. Voicing ethical concerns about AI can also help, as Anthropic has positioned itself as a thoughtful steward of this technology and values people who think about the downsides and risks.
Culture Fit Questions:
Asked at Asked at Anthropic • Behavioral interview
The interviewer will want to know the technical details and the deliverables of each project, as well as a rundown of who you worked with, particularly team members with other skills or backgrounds.
If you have a focus in a particular domain or industry, they’ll want to know more about it and what makes it unique. They may also ask about budgeting, project timelines, and how you managed internal and client expectations.
These discussions tend to get deep and granular. Once you get beyond the project details, they will ask how you handled the technical details, in particular, performance tuning, complexity, and scale.
As in the prior round, being willing to discuss issues, failures, and discomfort can help you, as long as you analyze the failures without ego and share what you learned. Vulnerability is something Anthropic values in any potential hire.
Behavioral Interview Questions:
Asked at Anthropic • ML design
This round will focus on a key role that ML engineers can play at Anthropic: serving as an advisor to clients. You’ll be asked to map out the implementation for a potential client with a set of regulatory and infrastructure requirements.
You might, for example, be asked to handle the implementation of an agent for a government contractor. You would need to account for the security constraints and compliance and advise them on how to protect sensitive training data while still offering value.
ML Design Questions:
Common Mistakes
ML/AI mistakes
- A lack of experience working with enterprise customers and understanding their needs and expectations
- Inexperience in building AI and ML solutions at scale. Failing to understand the infrastructural and performance demands of large-scale deployment
- A lack of expertise or knowledge of AI safeguards, data governance constraints, and other security techniques
- Being unwilling to dig into the details of performance tuning
- Failing to effectively utilize Claude Sonnet during technical rounds
- Not speaking up about tradeoffs and downsides, or frequently leaning towards an approach that cuts corners, particularly around safety or security
Behavioral and cultural mistakes
- A lack of interest in or understanding of the implications of widespread AI adoption
- Unwillingness or difficulty disclosing discomfort, a failure to embrace emotional or personal vulnerability
- An inability to learn from past experiences and mistakes, especially those that involved difficult ethical considerations, executive pressure, or disagreement
- Approaching cross-functional projects with an ego, failing to listen to or understand other perspectives
- Having a work history that favors speed over experimentation and consideration
- An inability to ‘go deep’ and examine past decisions without bias
Interview Prep
Learn what makes Anthropic unique. Anthropic has worked hard to create a model and message that stands in contrast with its AI peers. Success means something different for them, and you’ll need to understand how this applies to your work as an ML engineer.
On the business side, this means understanding the needs and challenges of enterprise AI. Anthropic’s business model is focused on the enterprise, and ML engineers are sometimes required to work closely with clients to assist with AI deployment and tuning.
On the cultural side, you should show that you will not let enthusiasm for AI override the legitimate risks and challenges these tools can create. Anthropic favors an approach that is more considered and deliberate than their peers, and they expect their teams to embrace this.
Practice honesty and transparency. You’ll be asked a lot of questions about your past work and will be expected to provide an honest and forthright assessment, even if it’s negative.
Taking a holistic view, even of work you are proud of, shows that you are willing to reassess things and consider how they could have gone better.
Dig into the details of agentic AI. Anthropic’s technical assessments will require some deep, detail-oriented work, particularly when analyzing large context windows in order to improve loop performance.
They won’t expect you to necessarily create the perfect solution within an hour, but you should be able to identify key opportunities for better performance and reliability, and to discuss the details of why you chose a particular approach.
About the Role
What do you focus on as an ML engineer at Anthropic?
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Create and implement enterprise-ready AI: Whether you’re working on infrastructure, safeguards, or research tools, you’ll be tasked with creating reliable and efficient agents and tools for the enterprise.
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Take on increasing complexity and scale: The demand for enterprise AI continues to grow, and you will work on new techniques and ideas to handle the increased infrastructural needs.
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Contribute to Athropic’s value proposition. Your work will be a proof of concept for Anthropic’s approach, affirming that a focus on ethics and sustainability can ultimately drive better outcomes.
Core Responsibilities
Anthropic’s ML Engineers are typically assigned to a particular team, product, or set of tools. In general, they will use their AI and ML skills to build infrastructure to improve model training and performance, context engineering, infrastructure efficiency, and safety. These are some general expectations:
- Create ML tools and infrastructure for batch processing, performance evaluation, and inference optimization
- Build tools for observability, safety monitoring, and other performance and security needs
- Analyze client feedback and use ML and AI tools to brainstorm solutions to any issues they describe
- Make recommendations to clients or potential clients to help them build out an AI system that incorporates their unique compliance, security, and infrastructure needs
Compensation
Anthropic’s ML Engineers enjoy some of the largest compensation packages in the AI model space.
Their careers site lists their annual salary between $320,000 to $405,000. And the total compensation packages are best-in-class.
Job Requirements
Experience
Candidates are expected to have 5+ years of experience working with ML. This includes experience with distributed systems, data pipelines and observability, and optimizing reliability and safety.
Education
Anthropic requires at least a bachelor’s degree in a relevant field, but will also accept relevant experience. They say on their careers site that only half their team members have a degree.
Resources
- Anthropic Careers Page
- Anthropic Engineering Blog
- Machine Learning Interview Prep
- Generative AI Interview Prep
- Anthropic Interview Questions
- Machine Learning Interview Questions
FAQs
Do I need to have AI experience to work as an ML engineer at Anthropic?
Anthropic acknowledges that, as a relatively new field, not every candidate will have had a chance to work directly on AI systems, and so they consider this a “nice to have” rather than a requirement.
That being said, using Claude Sonnet will be a component of the interview process, so some solid familiarity with AI coding assistants is necessary.
Does Anthropic have internships?
No, Anthropic is currently only hiring for full-time roles.
Are Anthropic’s ML Engineers assigned to a team, or can you apply directly to a team?
Their careers page has postings for specific ML engineer roles, such as research tooling or infrastructure, but if you apply through a connection on a job board or referral, you will be ‘routed’ to a particular team based on your experience.
How long does the Anthropic ML Engineer application process take?
Anthropic is a startup and does not have a rigidly defined recruitment process like other companies. Candidates have reported these interviews taking between 1 and 4 weeks.
If I am rejected, how long do I need to wait to reapply at Anthropic?
They say that rejected candidates should wait 12 months, but they will consider an earlier application if something specific has changed about the candidate’s situation.
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