AIUC Labs Research
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have "About the role" heading for description of role? The posting includes "The Role" section describing role. Also "In This Role, You Will" bullet list. Also "What we’re looking for" list of requirements. Also "Nice to have". Also "Our Values". Also "Compensation & Perks". Also maybe "Pay" not explicit but compensation details given: Competitive annual salary, Equity, Health insurance, Visa sponsorship, relocation stipend, Daily lunch, dinner & snacks. So we can have Benefits section for those. Pay maybe not explicit salary number; but we have "Competitive annual salary" and "Equity". Could put under Benefits or Pay? The instruction: include Pay/Schedule/Benefits sections only if posting actually states them. It states compensation & perks, so we can have Benefits section. Pay maybe not explicit number, but we have "Competitive annual salary" which is pay info. Could include Pay section with that line. Schedule not mentioned. So omit Schedule.
We need to avoid filler like "commensurate with experience". Not present.
We need to use - for any list of duties/requirements/benefits. Use
for prose.
We must not repeat job title as heading. No job title given explicitly; but we can ignore.
We must not include cookie/navigation/legal/EEO etc. Not present.
We need to preserve all substantive information; do not invent or summarize away detail.
Thus we need to extract:
- About the role: maybe combine "About AIUC" and "The Role" description? The heading "About the role" could contain description of role and company mission. We'll put prose.
- Responsibilities: from "In This Role, You Will" bullet list.
- Requirements: from "What we’re looking for" bullet list (the first set). Also maybe "Nice to have" could be under Requirements? But we could have separate "Qualifications" for nice-to-have? The instruction includes Qualifications heading. Could treat "Nice to have" as Qualifications. Or we could put them under Requirements as separate list? Better to follow headings: Requirements (must-have), Qualifications (nice-to-have). Skills maybe separate? Could combine.
We have "You’re a strong engineer, and can design and independently build, debug, and maintain research code and experiment pipelines. You can read an ML paper and implement it quickly, and extend or adapt it to a new research question. You can iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next. You have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research."
That's requirements.
Nice to have: list of items.
Skills: maybe we could list specific skills like ML, CS, etc. But we already have that in requirements. Could create Skills section summarizing technical skills: ML, CS, experiment pipelines, etc. But we must not invent or summarize away detail; we can extract from text.
Better to have:
- About the role: prose about company mission and role.
- Responsibilities: bullet list from "In This Role, You Will".
- Requirements: bullet list from "What we’re looking for" (the must-have).
- Qualifications: bullet list from "Nice to have".
- Benefits: bullet list from "Compensation & Perks".
- Pay: maybe include "Competitive annual salary" and "Equity (significant part of total compensation)" as prose or list.
But we already have Benefits includes those; maybe we separate Pay and Benefits: Pay: Competitive annual salary, Equity. Benefits: Health, dental and vision insurance, Visa sponsorship and relocation stipend, Daily lunch, dinner & snacks.
We need to ensure we don't duplicate.
Let's extract exact phrases:
About AIUC paragraph:
"About AIUC AIUC builds standards and insurance for frontier AI. Our mission is to Underwrite superintelligence. We exist because risk, not capability, is becoming the binding constraint on AI being useful. History offers lessons for how to build confidence in new technology; electricity burned down houses until insurers funded Underwriters Laboratories (UL) to certify products against their standards. Today, the UL mark is a trustmark on every light bulb in America. We’re building that for AI. AIUC-1, our standard for agent security, serves frontier builders like Cursor, Lovable, ElevenLabs, and Harvey. We've raised $55m from Ribbit, First Harmonic and Nat Friedman to audit frontier AI. Our team comes from METR, Anthropic, McKinsey, and OpenAI. Come join us."
The Role paragraph:
"The Role Superintelligence raises imminent questions: What models are safe to be released? How is the progress of recursive self-improvement audited? How should lab incentives be shaped? Who governs self-sovereign agents and what should incident forensics look like? Fortunately, researchers, auditors, hackers and actuaries have grappled with older versions of these questions for decades across nuclear energy, electricity, and car safety. AIUC applies that same combination to AI. AIUC-1 lets us underwrite the world's first agent insurance policy. We hold eval data across the most-used AI agents, a team with both frontier AI and traditional risk experience, and a consortium of Fortune 1000 security leaders. Answering these questions in time means combining frontier AI research with risk tools that already work. That's why we're founding AIUC Labs – a research lab which will exist so that AIUC and the world at large is on the frontier in navigating AGI. You will work directly with the founders to build out a new function and publish research that will guide the industry on what’s next."
In This Role, You Will bullet list (looks like sentences separated by line breaks? Actually it's a paragraph with colon then list separated by line breaks? The text:
"In This Role, You Will Own the questions that decide the strategic direction of AIUC: where AI capability & risk goes over the next 3-5 years, what replaces evals as agents get more capable, whether models behave differently when they know they are being tested, and how AI shifts the offense–defense balance in securityTake confidence infrastructure upstream to the model layer, where independent standards, rigorous evaluation and third-party assurance have to meet the release decisions labs are making nowTurn what AIUC has actually done into evidence, publish research and shape our public research programme, including the podcast and writing, that increases our influence in the space"
It seems missing punctuation; but we can treat as three bullet points:
- Own the questions that decide the strategic direction of AIUC: where AI capability & risk goes over the next 3-5 years, what replaces evals as agents get more capable, whether models behave differently when they know they are being tested, and how AI shifts the offense–defense balance in security
- Take confidence infrastructure upstream to the model layer, where independent standards, rigorous evaluation and third-party assurance have to meet the release decisions labs are making now
- Turn what AIUC has actually done into evidence, publish research and shape our public research programme, including the podcast and writing, that increases our influence in the space
We'll make
- for each.
What we’re looking for bullet list:
"You’re a strong engineer, and can design and independently build, debug, and maintain research code and experiment pipelines.You can read an ML paper and implement it quickly, and extend or adapt it to a new research question.You can iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next.You have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research."
We can split into four bullet points:
- Design and independently build, debug, and maintain research code and experiment pipelines.
- Read an ML paper and implement it quickly, and extend or adapt it to a new research question.
- Iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next.
- Have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research.
Nice to have bullet list:
"You might be coming from a frontier lab, a third-party evaluation organisation, or an ML team at a technology company in e.g., science of evals, security research, forecasting, or interpretability.You’re familiar with the model training pipeline, and have trained, fine-tuned, probed models, or built infrastructure that makes experimentation scalable.Published work that people in the field actually engaged with: a first-author paper, a benchmark others adopted, shifted how a community thought about something, was built into a product, or changing how real systems get deployedRange across the philosopher-to-engineer spectrum — comfortable arguing about where capability is heading in three years, and comfortable writing the code that tests whether you're right."
We need to split:
- Coming from a frontier lab, a third-party evaluation organisation, or an ML team at a technology company in e.g., science of evals, security research, forecasting, or interpretability.
- Familiar with the model training pipeline, and have trained, fine-tuned, probed models, or built infrastructure that makes experimentation scalable.
- Published work that people in the field actually engaged with: a first-author paper, a benchmark others adopted, shifted how a community thought about something, was built into a product, or changing how real systems get deployed.
- Range across the philosopher-to-engineer spectrum — comfortable arguing about where capability is heading in three years, and comfortable writing the code that tests whether you're right.
Our Values paragraph: maybe not needed as heading; but could be omitted as not in list of headings. The instruction says use those headings only if content exists. Values not in list; we could omit or maybe include under About the role? Better to omit as not requested.
Compensation & Perks bullet list:
"Competitive annual salaryEquity (significant part of total compensation)Health, dental and vision insurance for you and your dependentsVisa sponsorship and relocation stipend to bring you to SF, if neededDaily lunch, dinner & snacks"
We need to split:
- Competitive annual salary
- Equity (significant part of total compensation)
- Health, dental and vision insurance for you and your dependents
- Visa sponsorship and relocation stipend to bring you to SF, if needed
- Daily lunch, dinner & snacks
Now we need to decide sections:
- About the role: combine About AIUC and The Role? Could be two paragraphs.
- Responsibilities: the three bullet points.
- Requirements: the four bullet points from "What we’re looking for".
- Qualifications: the four bullet points from "Nice to have".
- Benefits: maybe health insurance, visa sponsorship, lunch etc.
- Pay: competitive annual salary, equity.
We must not create Pay section if not explicit salary number; but we have "Competitive annual salary" which is pay info. So we can include Pay.
We must not create Schedule section.
Now output HTML fragment: use
for each section heading, then content: either
for prose or
- for lists.
We need to avoid duplicate content. Ensure we preserve all substantive info.
Let's craft.
About the role: two paragraphs: first about AIUC, second about The Role.
We'll wrap each in
.
Responsibilities:
for prose. We must not repeat job title as heading. No job title given explicitly; but we can ignore. We must not include cookie/navigation/legal/EEO etc. Not present. We need to preserve all substantive information; do not invent or summarize away detail. Thus we need to extract: - About the role: maybe combine "About AIUC" and "The Role" description? The heading "About the role" could contain description of role and company mission. We'll put prose. - Responsibilities: from "In This Role, You Will" bullet list. - Requirements: from "What we’re looking for" bullet list (the first set). Also maybe "Nice to have" could be under Requirements? But we could have separate "Qualifications" for nice-to-have? The instruction includes Qualifications heading. Could treat "Nice to have" as Qualifications. Or we could put them under Requirements as separate list? Better to follow headings: Requirements (must-have), Qualifications (nice-to-have). Skills maybe separate? Could combine. We have "You’re a strong engineer, and can design and independently build, debug, and maintain research code and experiment pipelines. You can read an ML paper and implement it quickly, and extend or adapt it to a new research question. You can iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next. You have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research." That's requirements. Nice to have: list of items. Skills: maybe we could list specific skills like ML, CS, etc. But we already have that in requirements. Could create Skills section summarizing technical skills: ML, CS, experiment pipelines, etc. But we must not invent or summarize away detail; we can extract from text. Better to have: - About the role: prose about company mission and role. - Responsibilities: bullet list from "In This Role, You Will". - Requirements: bullet list from "What we’re looking for" (the must-have). - Qualifications: bullet list from "Nice to have". - Benefits: bullet list from "Compensation & Perks". - Pay: maybe include "Competitive annual salary" and "Equity (significant part of total compensation)" as prose or list. But we already have Benefits includes those; maybe we separate Pay and Benefits: Pay: Competitive annual salary, Equity. Benefits: Health, dental and vision insurance, Visa sponsorship and relocation stipend, Daily lunch, dinner & snacks. We need to ensure we don't duplicate. Let's extract exact phrases: About AIUC paragraph: "About AIUC AIUC builds standards and insurance for frontier AI. Our mission is to Underwrite superintelligence. We exist because risk, not capability, is becoming the binding constraint on AI being useful. History offers lessons for how to build confidence in new technology; electricity burned down houses until insurers funded Underwriters Laboratories (UL) to certify products against their standards. Today, the UL mark is a trustmark on every light bulb in America. We’re building that for AI. AIUC-1, our standard for agent security, serves frontier builders like Cursor, Lovable, ElevenLabs, and Harvey. We've raised $55m from Ribbit, First Harmonic and Nat Friedman to audit frontier AI. Our team comes from METR, Anthropic, McKinsey, and OpenAI. Come join us." The Role paragraph: "The Role Superintelligence raises imminent questions: What models are safe to be released? How is the progress of recursive self-improvement audited? How should lab incentives be shaped? Who governs self-sovereign agents and what should incident forensics look like? Fortunately, researchers, auditors, hackers and actuaries have grappled with older versions of these questions for decades across nuclear energy, electricity, and car safety. AIUC applies that same combination to AI. AIUC-1 lets us underwrite the world's first agent insurance policy. We hold eval data across the most-used AI agents, a team with both frontier AI and traditional risk experience, and a consortium of Fortune 1000 security leaders. Answering these questions in time means combining frontier AI research with risk tools that already work. That's why we're founding AIUC Labs – a research lab which will exist so that AIUC and the world at large is on the frontier in navigating AGI. You will work directly with the founders to build out a new function and publish research that will guide the industry on what’s next." In This Role, You Will bullet list (looks like sentences separated by line breaks? Actually it's a paragraph with colon then list separated by line breaks? The text: "In This Role, You Will Own the questions that decide the strategic direction of AIUC: where AI capability & risk goes over the next 3-5 years, what replaces evals as agents get more capable, whether models behave differently when they know they are being tested, and how AI shifts the offense–defense balance in securityTake confidence infrastructure upstream to the model layer, where independent standards, rigorous evaluation and third-party assurance have to meet the release decisions labs are making nowTurn what AIUC has actually done into evidence, publish research and shape our public research programme, including the podcast and writing, that increases our influence in the space" It seems missing punctuation; but we can treat as three bullet points: - Own the questions that decide the strategic direction of AIUC: where AI capability & risk goes over the next 3-5 years, what replaces evals as agents get more capable, whether models behave differently when they know they are being tested, and how AI shifts the offense–defense balance in security - Take confidence infrastructure upstream to the model layer, where independent standards, rigorous evaluation and third-party assurance have to meet the release decisions labs are making now - Turn what AIUC has actually done into evidence, publish research and shape our public research programme, including the podcast and writing, that increases our influence in the space We'll make
- for each.
What we’re looking for bullet list:
"You’re a strong engineer, and can design and independently build, debug, and maintain research code and experiment pipelines.You can read an ML paper and implement it quickly, and extend or adapt it to a new research question.You can iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next.You have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research."
We can split into four bullet points:
- Design and independently build, debug, and maintain research code and experiment pipelines.
- Read an ML paper and implement it quickly, and extend or adapt it to a new research question.
- Iterate and experiment quickly, such as working through an ambiguous open-ended question and turning into a tractable experiment, choosing baselines, and investigating unexpected details, as well as deciding what comes next.
- Have a strong background in ML, CS, or an adjacent quantitative field, developed through a combination of engineering and research.
Nice to have bullet list:
"You might be coming from a frontier lab, a third-party evaluation organisation, or an ML team at a technology company in e.g., science of evals, security research, forecasting, or interpretability.You’re familiar with the model training pipeline, and have trained, fine-tuned, probed models, or built infrastructure that makes experimentation scalable.Published work that people in the field actually engaged with: a first-author paper, a benchmark others adopted, shifted how a community thought about something, was built into a product, or changing how real systems get deployedRange across the philosopher-to-engineer spectrum — comfortable arguing about where capability is heading in three years, and comfortable writing the code that tests whether you're right."
We need to split:
- Coming from a frontier lab, a third-party evaluation organisation, or an ML team at a technology company in e.g., science of evals, security research, forecasting, or interpretability.
- Familiar with the model training pipeline, and have trained, fine-tuned, probed models, or built infrastructure that makes experimentation scalable.
- Published work that people in the field actually engaged with: a first-author paper, a benchmark others adopted, shifted how a community thought about something, was built into a product, or changing how real systems get deployed.
- Range across the philosopher-to-engineer spectrum — comfortable arguing about where capability is heading in three years, and comfortable writing the code that tests whether you're right.
Our Values paragraph: maybe not needed as heading; but could be omitted as not in list of headings. The instruction says use those headings only if content exists. Values not in list; we could omit or maybe include under About the role? Better to omit as not requested.
Compensation & Perks bullet list:
"Competitive annual salaryEquity (significant part of total compensation)Health, dental and vision insurance for you and your dependentsVisa sponsorship and relocation stipend to bring you to SF, if neededDaily lunch, dinner & snacks"
We need to split:
- Competitive annual salary
- Equity (significant part of total compensation)
- Health, dental and vision insurance for you and your dependents
- Visa sponsorship and relocation stipend to bring you to SF, if needed
- Daily lunch, dinner & snacks
Now we need to decide sections:
- About the role: combine About AIUC and The Role? Could be two paragraphs.
- Responsibilities: the three bullet points.
- Requirements: the four bullet points from "What we’re looking for".
- Qualifications: the four bullet points from "Nice to have".
- Benefits: maybe health insurance, visa sponsorship, lunch etc.
- Pay: competitive annual salary, equity.
We must not create Pay section if not explicit salary number; but we have "Competitive annual salary" which is pay info. So we can include Pay.
We must not create Schedule section.
Now output HTML fragment: use
for each section heading, then content: either
for prose or
- for lists.
We need to avoid duplicate content. Ensure we preserve all substantive info.
Let's craft.
About the role: two paragraphs: first about AIUC, second about The Role.
We'll wrap each in
. Responsibilities:
- for lists.
We need to avoid duplicate content. Ensure we preserve all substantive info.
Let's craft.
About the role: two paragraphs: first about AIUC, second about The Role.
We'll wrap each in