Recruiter (Research)
for section headings. No job title heading. No Job ID. Remove boilerplate. Keep substantive info.
We need sections: About Us (maybe "About the role"? Actually heading "About the role" is a section heading. The posting includes "About Us" and "You will:" etc. We can map: "About the role" could contain description of role? But we have "About Us" separate. The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to decide which sections exist.
We have content: TL;DR: We’re looking for an AI Research Recruiter... (intro). Then "About Us" paragraph. Then "You will:" list of responsibilities. Then "You’ll fit right in if you:" list of qualifications/requirements. Then "A big plus:" list of preferred qualifications. Then "Why White Circle" list of benefits. Then "How we hire" process (maybe not needed? It's part of hiring process but not a section heading; could be omitted or placed under maybe "Hiring Process"? Not in list of allowed headings; but we can omit if not needed. The instruction: include a section ONLY when posting has real, specific content for it. So we could include sections: About the role (maybe combine TL;DR and About Us?), Responsibilities (You will), Requirements/Qualifications (You’ll fit right in if you), Skills (maybe A big plus?), Benefits (Why White Circle). Pay and Schedule not present, so omit.
We must not repeat job title as heading. So no heading for "AI Research Recruiter". Use for each section.
We need to preserve all substantive info, not invent or summarize away detail. Use - for any list of duties/requirements/benefits, and
for prose.
We need to remove cookie/navigation/legal/EEO etc. Not present.
Let's extract content.
Intro: "TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety."
That's prose about role.
About Us: paragraph(s). Let's copy:
"White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need."
Note there are missing spaces after periods due to formatting but we keep as is.
Responsibilities (You will:):
List items:
- Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
- Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
- Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
- Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
- Evaluate candidates’ actual contributions
- Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
- Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
- Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
- Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
- Support hiring outside your primary stream when business priorities require it
Qualifications/Requirements (You’ll fit right in if you:):
List items:
- Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
- Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
- Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
- Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
- Can interpret technical profiles beyond keywords, job titles, or company names
- Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
- Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
- Participate in or closely follow AI research and engineering communities
- Can build credibility with highly technical candidates and communicate nuanced research problems accurately
- Have experience recruiting across the US and European markets
- Can act as a trusted talent advisor to technical hiring managers and research leadership
- Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
- Are willing and able to recruit beyond Research roles when needed
- Speak and write fluent English (C1+)
A big plus: (Preferred qualifications):
List items:
- Experience recruiting for a frontier AI lab, AI safety org, research-focused startup, or leading AI company
- A strong existing network among AI researchers, research engineers, and ML infra specialists
- Experience sourcing through publications, conference programs, citations, GitHub, open-source projects, research communities, and academic labs
- A technical or research background in computer science, machine learning, mathematics, or a related field
- Experience recruiting for an early-stage startup where the research direction and hiring requirements evolve quickly
Why White Circle (Benefits):
List items:
- Meaningful equity package
- Paid time off in line with your local regulations, no matter where you work from
- All the hardware, tools, and services you need
- Covered subscriptions for AI agents
- Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez
How we hire: maybe we could include as a section "Hiring Process" but not in allowed headings. Could omit. The instruction: include a section ONLY when posting has real, specific content for it — never emit a heading followed by filler/placeholder text. Since "How we hire" is not in the list of allowed headings, we could either omit or treat as a section with heading maybe "Hiring Process". But the allowed headings list is just examples; we could still use other headings? The instruction says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies those are the expected headings; we should stick to those. So we omit hiring process.
Now we need to decide which sections to include: About the role (maybe combine TL;DR and About Us?), Responsibilities, Requirements (or Qualifications), Skills (maybe A big plus?), Benefits (Why White Circle). Could also have Qualifications separate from Requirements? The list includes both Requirements and Qualifications as possible headings. We could use "Requirements" for the must-have list, and "Skills" for the big plus list. Or we could use "Qualifications" for must-have and "Skills" for plus. Let's map:
- About the role: TL;DR + About Us (maybe two paragraphs)
- Responsibilities: You will list
- Requirements: You’ll fit right in if you list (must-have)
- Skills: A big plus list (preferred)
- Benefits: Why White Circle list
We must not repeat job title as heading. Good.
Now produce HTML fragment:
About the role
TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety.
White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.
Responsibilities
- Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
- Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
- Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
- Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
- Evaluate candidates’ actual contributions
- Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
- Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
- Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
- Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
- Support hiring outside your primary stream when business priorities require it
Requirements
- Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
- Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
- Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
- Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
- Can interpret technical profiles beyond keywords, job titles, or company names
- Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
- Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
- Participate in or closely follow AI research and engineering communities
- Can build credibility with highly technical candidates and communicate nuanced research problems accurately
- Have experience recruiting across the US and European markets
- Can act as a trusted talent advisor to technical hiring managers and research leadership
- Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
- Are willing and able to
for each section.
We need to preserve all substantive info, not invent or summarize away detail. Use - for any list of duties/requirements/benefits, and
for prose.
We need to remove cookie/navigation/legal/EEO etc. Not present.
Let's extract content.
Intro: "TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety."
That's prose about role.
About Us: paragraph(s). Let's copy:
"White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need."
Note there are missing spaces after periods due to formatting but we keep as is.
Responsibilities (You will:):
List items:
- Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
- Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
- Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
- Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
- Evaluate candidates’ actual contributions
- Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
- Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
- Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
- Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
- Support hiring outside your primary stream when business priorities require it
Qualifications/Requirements (You’ll fit right in if you:):
List items:
- Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
- Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
- Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
- Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
- Can interpret technical profiles beyond keywords, job titles, or company names
- Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
- Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
- Participate in or closely follow AI research and engineering communities
- Can build credibility with highly technical candidates and communicate nuanced research problems accurately
- Have experience recruiting across the US and European markets
- Can act as a trusted talent advisor to technical hiring managers and research leadership
- Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
- Are willing and able to recruit beyond Research roles when needed
- Speak and write fluent English (C1+)
A big plus: (Preferred qualifications):
List items:
- Experience recruiting for a frontier AI lab, AI safety org, research-focused startup, or leading AI company
- A strong existing network among AI researchers, research engineers, and ML infra specialists
- Experience sourcing through publications, conference programs, citations, GitHub, open-source projects, research communities, and academic labs
- A technical or research background in computer science, machine learning, mathematics, or a related field
- Experience recruiting for an early-stage startup where the research direction and hiring requirements evolve quickly
Why White Circle (Benefits):
List items:
- Meaningful equity package
- Paid time off in line with your local regulations, no matter where you work from
- All the hardware, tools, and services you need
- Covered subscriptions for AI agents
- Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez
How we hire: maybe we could include as a section "Hiring Process" but not in allowed headings. Could omit. The instruction: include a section ONLY when posting has real, specific content for it — never emit a heading followed by filler/placeholder text. Since "How we hire" is not in the list of allowed headings, we could either omit or treat as a section with heading maybe "Hiring Process". But the allowed headings list is just examples; we could still use other headings? The instruction says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies those are the expected headings; we should stick to those. So we omit hiring process.
Now we need to decide which sections to include: About the role (maybe combine TL;DR and About Us?), Responsibilities, Requirements (or Qualifications), Skills (maybe A big plus?), Benefits (Why White Circle). Could also have Qualifications separate from Requirements? The list includes both Requirements and Qualifications as possible headings. We could use "Requirements" for the must-have list, and "Skills" for the big plus list. Or we could use "Qualifications" for must-have and "Skills" for plus. Let's map:
- About the role: TL;DR + About Us (maybe two paragraphs)
- Responsibilities: You will list
- Requirements: You’ll fit right in if you list (must-have)
- Skills: A big plus list (preferred)
- Benefits: Why White Circle list
We must not repeat job title as heading. Good.
Now produce HTML fragment:
About the role
TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety.
White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.
Responsibilities
- Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
- Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
- Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
- Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
- Evaluate candidates’ actual contributions
- Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
- Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
- Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
- Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
- Support hiring outside your primary stream when business priorities require it
Requirements
- Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
- Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
- Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
- Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
- Can interpret technical profiles beyond keywords, job titles, or company names
- Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
- Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
- Participate in or closely follow AI research and engineering communities
- Can build credibility with highly technical candidates and communicate nuanced research problems accurately
- Have experience recruiting across the US and European markets
- Can act as a trusted talent advisor to technical hiring managers and research leadership
- Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
- Are willing and able to
for prose. We need to remove cookie/navigation/legal/EEO etc. Not present. Let's extract content. Intro: "TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety." That's prose about role. About Us: paragraph(s). Let's copy: "White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need." Note there are missing spaces after periods due to formatting but we keep as is. Responsibilities (You will:): List items: - Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles - Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them - Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks - Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly - Evaluate candidates’ actual contributions - Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful - Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact - Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities - Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows - Support hiring outside your primary stream when business priorities require it Qualifications/Requirements (You’ll fit right in if you:): List items: - Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent - Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer - Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ - Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra - Can interpret technical profiles beyond keywords, job titles, or company names - Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems - Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels - Participate in or closely follow AI research and engineering communities - Can build credibility with highly technical candidates and communicate nuanced research problems accurately - Have experience recruiting across the US and European markets - Can act as a trusted talent advisor to technical hiring managers and research leadership - Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve - Are willing and able to recruit beyond Research roles when needed - Speak and write fluent English (C1+) A big plus: (Preferred qualifications): List items: - Experience recruiting for a frontier AI lab, AI safety org, research-focused startup, or leading AI company - A strong existing network among AI researchers, research engineers, and ML infra specialists - Experience sourcing through publications, conference programs, citations, GitHub, open-source projects, research communities, and academic labs - A technical or research background in computer science, machine learning, mathematics, or a related field - Experience recruiting for an early-stage startup where the research direction and hiring requirements evolve quickly Why White Circle (Benefits): List items: - Meaningful equity package - Paid time off in line with your local regulations, no matter where you work from - All the hardware, tools, and services you need - Covered subscriptions for AI agents - Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez How we hire: maybe we could include as a section "Hiring Process" but not in allowed headings. Could omit. The instruction: include a section ONLY when posting has real, specific content for it — never emit a heading followed by filler/placeholder text. Since "How we hire" is not in the list of allowed headings, we could either omit or treat as a section with heading maybe "Hiring Process". But the allowed headings list is just examples; we could still use other headings? The instruction says use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies those are the expected headings; we should stick to those. So we omit hiring process.
Now we need to decide which sections to include: About the role (maybe combine TL;DR and About Us?), Responsibilities, Requirements (or Qualifications), Skills (maybe A big plus?), Benefits (Why White Circle). Could also have Qualifications separate from Requirements? The list includes both Requirements and Qualifications as possible headings. We could use "Requirements" for the must-have list, and "Skills" for the big plus list. Or we could use "Qualifications" for must-have and "Skills" for plus. Let's map:
- About the role: TL;DR + About Us (maybe two paragraphs)
- Responsibilities: You will list
- Requirements: You’ll fit right in if you list (must-have)
- Skills: A big plus list (preferred)
- Benefits: Why White Circle list
We must not repeat job title as heading. Good.
Now produce HTML fragment:
About the role
TL;DR: We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety.
White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale. We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and othersWe process over one hundred million API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.
Responsibilities
- Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
- Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
- Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
- Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
- Evaluate candidates’ actual contributions
- Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
- Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
- Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
- Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
- Support hiring outside your primary stream when business priorities require it
Requirements
- Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
- Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
- Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
- Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
- Can interpret technical profiles beyond keywords, job titles, or company names
- Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
- Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
- Participate in or closely follow AI research and engineering communities
- Can build credibility with highly technical candidates and communicate nuanced research problems accurately
- Have experience recruiting across the US and European markets
- Can act as a trusted talent advisor to technical hiring managers and research leadership
- Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
- Are willing and able to