- Страна
- США
- Зарплата
- 280 000 $ – 320 000 $
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Applied AI Engineer, Life Sciences (Beneficial Deployments)
Исключительная вакансия в одной из ведущих ИИ-лабораторий мира с очень высокой зарплатой и возможностью влиять на развитие науки. Идеально для топовых инженеров, увлеченных биологией.
Сложность вакансии
Роль требует редкого сочетания навыков: глубокой экспертизы в Life Sciences (геномика, нейронауки) и практического опыта разработки сложных ИИ-систем на базе LLM. Высокая планка ответственности при работе с ведущими научными институтами мира.
Анализ зарплаты
Предлагаемая зарплата ($280k–$320k) находится на верхней границе рынка для Senior/Staff AI ролей в США, особенно учитывая статус Anthropic как 'tier-1' работодателя. Это значительно выше средних показателей по рынку для обычных Software Engineers.
Сопроводительное письмо
I am writing to express my strong interest in the Applied AI Engineer position within the Beneficial Deployments team at Anthropic. With over four years of experience in shipping production-grade systems and a deep background in scientific computing, I am particularly drawn to your mission of accelerating scientific progress through the strategic deployment of Claude. My experience bridges the gap between complex LLM architectures and the rigorous requirements of life sciences research, making me well-equipped to partner with institutions like HHMI and the Allen Institute.
In my previous roles, I have focused on building LLM-powered tools that prioritize reliability and interpretability—core tenets of Anthropic’s philosophy. I am excited by the prospect of developing reusable ecosystem infrastructure, such as MCP servers and domain-specific benchmarks, to lower the barrier for AI adoption in genomics and drug discovery. I am a 'scrappy' builder who thrives in ambiguous environments and is committed to ensuring that the benefits of frontier AI are accessible to the scientific communities that can drive the most significant societal impact.
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Описание вакансии
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 Beneficial Deployments:
Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences — focusing on raising the floor for those who need it most.
About the Role:
We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R&D through translation by an order of magnitude. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimaging pharma pipelines — and building the technical infrastructure to back that up.
You'll work directly with flagship research partners like Howard Hughes Medical Institute and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside — you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments applied AI team focused on bringing more of life sciences closer to the frontier and be responsible for building with our partners.
Responsibilities:
- Partner deeply with flagship life sciences research institutions — understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.
- Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.
- Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.
- Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding.
You Might Be a Good Fit If You Have:
- 4+ years as a Software Engineer, Forward Deployed Engineer, or technical founder — with production experience shipping systems that real users depend on.
- Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.
- Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.
- Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder.
- A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission.
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:
$280,000—$320,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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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Навыки
- Genomics
- Python
- LLM
- Software Engineering
- Prompt Engineering
- MCP
- Scientific Computing
- Biomedical Research
- Agent Architectures
Возможные вопросы на собеседовании
Проверка способности интегрировать ИИ в специфические научные процессы.
Опишите ваш опыт работы с данными в области Life Sciences (например, геномные данные или медицинские публикации). С какими основными трудностями вы сталкивались при их обработке?
Оценка технических навыков в области LLM и архитектуры агентов.
Как бы вы спроектировали систему ИИ-агентов для автоматизации анализа экспериментальных данных в лаборатории, учитывая требования к воспроизводимости и точности?
Проверка понимания концепции Model Context Protocol (MCP), упомянутой в вакансии.
Как вы видите роль протокола MCP в создании масштабируемой инфраструктуры для научных исследований? Какие серверы вы бы реализовали в первую очередь?
Оценка навыков взаимодействия с партнерами (Forward Deployed подход).
Расскажите о случае, когда вам приходилось внедрять технически сложное решение в команду, которая не была знакома с ИИ. Как вы преодолевали сопротивление или недоверие?
Проверка соответствия миссии безопасности и этики Anthropic.
В контексте Life Sciences, какие риски безопасности ИИ вы считаете наиболее критическими и как их можно минимизировать на уровне архитектуры приложения?
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- Страна
- США
- Зарплата
- 280 000 $ – 320 000 $