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Practising for the listing Research Scientist/Engineer, Biological Safety. Its requirements are filled in below. When you have an answer you like, paste it with your CV in Career Mirror to see which of the job's skills it shows.

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Research Scientist/Engineer, Biological Safety
About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

We are looking for research engineers to build the safety and oversight mechanisms that govern how our models handle biological knowledge. As a bio safety researcher, you will spend your time: designing and running capability evaluations against frontier models, generating and curating training data for our safety classifiers, training and iterating on those classifiers alongside our ML engineers, and measuring how they hold up against adversarial pressure in production traffic.

This work sits at the intersection of applied ML and biosecurity. You will help define what responsible AI safety looks like in the biological domain, translating threat models into evals, datasets, and deployed systems. The core technical tension you will own is precision: safeguards need to be robust against sophisticated actors while staying out of the way of the far larger population of legitimate researchers using Claude to accelerate life sciences work. Getting that tradeoff right is an empirical problem, and you will be the person measuring it.

You do not need to be an ML researcher today. We are looking for strong scientific programmers with real depth in modern biology who want to bring that depth to bear on model evaluation and classifier development.

Key responsibilities

• Design, build, and run capability evaluations to assess what new models can do in the biological domain, and turn results into concrete deployment recommendations

• Develop training and evaluation datasets for our safety classifiers, working with internal and external threat modeling experts to ground them in realistic risk

• Train, tune, and iterate on safety classifiers with ML engineers, optimizing jointly for adversarial robustness and low false-positive rates

• Build the tooling and pipelines that make evaluation and classifier development fast and repeatable

• Analyze classifier and eval performance against production traffic, identify gaps, and prioritize improvements

• Design and run red-teaming and stress-testing of safeguards as threats, models, and product surfaces evolve

• Partner with Research, Product, and Policy teams to embed biological safety throughout the model development lifecycle

• Contribute to external communications including model cards, blog posts, and policy documents

• Track developments in biology, ML, and biosecurity for their potential to create new risks or enable new mitigations

Minimum qualifications

• Strong proficiency in Python, and extensive background in scientific programming and data analysis skills

• Excellent grasp of ML fundamentals and an ability to rapidly adopt ML development practices

• Excellent knowledge of modern biology across both measurement and engineering: high-throughput assays and functional characterization, as well as gene synthesis, genome editing, strain construction, and protein engineering

• Ability to build and maintain your own tooling rather than relying on others to implement your ideas

• Experience designing quantitative experiments or evaluations and drawing defensible conclusions from noisy results

• Strong analytical and writing skills, and the ability to explain technical concepts to non-technical stakeholders

• Familiarity with dual-use research concerns and biosecurity frameworks, such as select agent regulations, the Biological Weapons Convention, or Australia Group guidelines

• Comfort with ambiguity and with shifting priorities as AI capabilities change

• Can work independently while maintaining strong collaboration with cross-functional teams

• Are results-oriented, with a bias towards flexibility and impact

• Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration

Preferred qualifications

• Experience working with large language models, including prompting, fine-tuning, or evaluation

• Experience training or deploying classifiers or other ML systems in production

• Experience developing ML methods for biological systems or biological data

• Familiarity with adversarial robustness, red-teaming, or safety evaluation of ML systems

• Have at least 8 years of hands-on experience in life sciences, with deep expertise in areas such as molecular biology, drug discovery, or computational biology

• Experience leading complex technical projects across multiple stakeholder groups

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:
$300,000 — $405,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

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Requirements found

Training (must) Python (must) Editing (must) Writing (must) Sales (nice) Recruiting

What this posting asks for

Level: Senior — The posting asks for 8 years of experience.

Main responsibilities

Design, build, and run capability evaluations to assess what new models can do in the biological domain, and turn results into concrete deployment recommenda…
Develop training and evaluation datasets for our safety classifiers, working with internal and external threat modeling experts to ground them in realistic risk
Train, tune, and iterate on safety classifiers with ML engineers, optimizing jointly for adversarial robustness and low false-positive rates
Build the tooling and pipelines that make evaluation and classifier development fast and repeatable
Analyze classifier and eval performance against production traffic, identify gaps, and prioritize improvements
Design and run red-teaming and stress-testing of safeguards as threats, models, and product surfaces evolve
Partner with Research, Product, and Policy teams to embed biological safety throughout the model development lifecycle
Contribute to external communications including model cards, blog posts, and policy documents
Track developments in biology, ML, and biosecurity for their potential to create new risks or enable new mitigations
Strong proficiency in Python, and extensive background in scientific programming and data analysis skills

Competencies an interviewer will look for

Communication Core
In the posting: “Contribute to external communications including model cards, blog posts, and policy documents” · “Strong analytical and writing skills, and the ability to explain technical concepts to non-technical stakeholders” · “If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.”
Show it by: Adapts the message to the listener; Gives an example of explaining something clearly in writing or speech.
Teamwork and collaboration Core
In the posting: “Partner with Research, Product, and Policy teams to embed biological safety throughout the model development lifecycle” · “Can work independently while maintaining strong collaboration with cross-functional teams” · “In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic.”
Show it by: Says what they did inside a team, not only what 'we' did; Shows how a disagreement was handled.
Planning and organisation Core
In the posting: “Analyze classifier and eval performance against production traffic, identify gaps, and prioritize improvements” · “Comfort with ambiguity and with shifting priorities as AI capabilities change” · “Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration”
Show it by: Shows how priorities were set when everything was urgent; Mentions deadlines met and how.
Data and analysis Core
In the posting: “As a bio safety researcher, you will spend your time: designing and running capability evaluations against frontier models, generating and curating training …” · “Analyze classifier and eval performance against production traffic, identify gaps, and prioritize improvements” · “Strong proficiency in Python, and extensive background in scientific programming and data analysis skills”
Show it by: Uses a number to describe the situation or the result; Explains what the data showed and what was done with it.
Adaptability and learning Core
In the posting: “Comfort with ambiguity and with shifting priorities as AI capabilities change” · “Are results-oriented, with a bias towards flexibility and impact” · “We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely…”
Show it by: Describes learning something new quickly and using it; Shows a change of plan and how they adjusted.
Attention to detail and quality Core
In the posting: “We are looking for research engineers to build the safety and oversight mechanisms that govern how our models handle biological knowledge.” · “As a bio safety researcher, you will spend your time: designing and running capability evaluations against frontier models, generating and curating training …” · “You will help define what responsible AI safety looks like in the biological domain, translating threat models into evals, datasets, and deployed systems.”
Show it by: Gives an example of an error caught before it caused harm; Names the check or standard they used.
Technical skill Core
In the posting: “Strong proficiency in Python, and extensive background in scientific programming and data analysis skills” · “Excellent knowledge of modern biology across both measurement and engineering: high-throughput assays and functional characterization, as well as gene synthe…”
Show it by: Names the tools, languages or systems used and how; Explains a technical choice and its trade-off.
Stakeholder management Core
In the posting: “Strong analytical and writing skills, and the ability to explain technical concepts to non-technical stakeholders” · “Experience leading complex technical projects across multiple stakeholder groups”
Show it by: Shows how they aligned people with different goals; Explains how they handled a 'no' from someone senior.
Ownership and accountability Supporting
In the posting: “Can work independently while maintaining strong collaboration with cross-functional teams”
Show it by: Takes a task from start to finish without being chased; Owns a mistake and what was changed after it.

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Practice questions

Tell me about yourself. Dedicated step-by-step practice.
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Built from this posting (8):
Tell me about a time you had to explain something complex to someone outside your field. From this posting
Describe a time you worked with another team to deliver something. What was your part? From this posting
You have three urgent tasks due today and time to finish only two. How do you decide? From this posting
Tell me about a time you used data or numbers to make a decision. From this posting
How have you used training in your work? Walk me through one real example. From this posting
Tell me about a problem you solved using python. From this posting
Tell me about a time you helped someone on your team get better at their work. From this posting
Tell me about a task you took from start to finish without anyone asking you to. From this posting
Tell me about a sale or agreement you are proud of.
Describe a time you handled an upset customer or colleague.
Tell me about a time you had several deadlines at once. How did you decide what to do first?
Give an example of a mistake you caught before it caused a problem.
Tell me about a time you taught someone how to do something.
Describe a time you took the lead without being asked.
Tell me about a time you had to explain something complicated in simple terms.
Describe a process you improved.
Do you have any questions for us?
Why do you want to work here?
Why is there a gap in your employment? Difficult question
Why did you leave your last job? Difficult question
Why are you changing careers? Difficult question
What is your greatest weakness? Difficult question
What are your salary expectations? Difficult question
You seem overqualified for this role. Why do you want it? Difficult question
Improper-question practice: How old are you? / Do you have children? / Where are you from originally?
A customer is angry about a delay you did not cause. What do you do?
How do you keep track of many open customer requests?
Tell me about a time you could not give a customer what they wanted.
You find that the inventory count does not match the system. What do you do?
Tell me about a time a shipment or delivery went wrong.
How do you make sure safety rules are followed on a busy shift?
Two managers ask you to book the same room at the same time. What do you do?
How have you used spreadsheets in your work?
Tell me about a time you had to enter or check a lot of data accurately.
A prospect says your price is too high. How do you respond?
How do you keep your pipeline organised?
Tell me about a technical problem you solved on your own.
You find a bug right before a release. What do you do?
Walk me through how you would check whether a report's numbers are right.