- Страна
- США
- Зарплата
- 320 000 $ – 485 000 $
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Privacy Research Engineer, Safeguards
Исключительная вакансия в одной из ведущих ИИ-компаний мира с очень высокой зарплатой и возможностью влиять на глобальные стандарты безопасности ИИ. Отличные условия релокации и работы в Сан-Франциско.
Сложность вакансии
Роль требует редкого сочетания глубоких знаний в области LLM и узкой специализации на методах сохранения приватности (Differential Privacy). Высокий порог входа обусловлен необходимостью иметь опыт работы с передовыми фреймворками и публикациями в топовых научных изданиях.
Анализ зарплаты
Предложенная зарплата ($320k - $485k) находится на верхнем пределе рынка даже для Сан-Франциско. Она значительно превышает средние показатели для Senior/Staff инженеров в обычных технологических компаниях, соответствуя уровню топовых ИИ-лабораторий (OpenAI, Google DeepMind).
Сопроводительное письмо
I am writing to express my strong interest in the Privacy Research Engineer position at Anthropic. With a deep background in privacy-preserving machine learning and extensive experience in developing robust training algorithms, I have long admired Anthropic’s commitment to building reliable and interpretable AI systems. My expertise in Differential Privacy, combined with a track record of shipping features in fast-paced environments, aligns perfectly with your mission to mitigate risks associated with frontier models.
In my previous work, I have successfully implemented privacy-first techniques and conducted rigorous audits of large-scale data usage. I am particularly drawn to Anthropic’s view of AI research as an empirical science and your collaborative approach to 'big science' challenges. I am eager to bring my skills in PyTorch and my experience with LLM training pipelines to help ensure that Anthropic’s models remain at the forefront of both performance and user data protection.
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Откликнитесь в anthropic уже сейчас
Присоединяйтесь к Anthropic, чтобы определять будущее безопасности и приватности ИИ в одной из самых влиятельных лабораторий мира!
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 the Role
We are looking for researchers to help mitigate the risks that come with building AI systems. One of these risks is the potential for models to interact with private user data. In this role, you'll design and implement privacy-preserving techniques, audit our current techniques, and set the direction for how Anthropic handles privacy more broadly.
Responsibilities:
- Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process
- Develop privacy-first training algorithms and techniques
- Develop evaluation and auditing techniques to measure the privacy of training algorithms
- Work with a small, senior team of engineers and researchers to enact a forward-looking privacy policy
- Advocate on behalf of our users to ensure responsible handling of all data
You may be a good fit if you have:
- Experience working on privacy-preserving machine learning
- A track record of shipping products and features inside a fast-moving environment
- Strong coding skills in Python and familiarity with ML frameworks like PyTorch or JAX.
- Deep familiarity with large language models, how they work, and how they are trained
- Have experience working with privacy-preserving techniques (e.g., differential privacy and how it is different from k-anonymity, l-diversity, and t-closeness)
- Experience supporting fast-paced startup engineering teams
- Demonstrated success in bringing clarity and ownership to ambiguous technical problems
- Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics
Strong candidates may also:
- Have published papers on the topic of privacy-preserving ML at top academic venues
- Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models)
- Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus)
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:
$320,000—$485,000 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
- Python
- PyTorch
- Large Language Models
- JAX
- Reinforcement Learning
- Machine Learning Safety
- Differential Privacy
- Opacus
- TensorFlow Privacy
Возможные вопросы на собеседовании
Проверка фундаментальных знаний в области приватности данных, упомянутых в вакансии.
Можете ли вы объяснить математическую разницу между Differential Privacy и k-anonymity, а также их применимость к обучению LLM?
Оценка практического опыта работы с инструментами, критически важными для роли.
Расскажите о вашем опыте использования Opacus или TF-Privacy: с какими основными трудностями при масштабировании на большие модели вы сталкивались?
Проверка понимания специфики обучения больших языковых моделей.
Как внедрение механизмов дифференциальной приватности влияет на сходимость модели и итоговое качество генерации (perplexity)?
Оценка способности работать в условиях неопределенности стартапа.
Опишите случай, когда вам пришлось внедрять инициативу по безопасности в условиях сжатых сроков и сопротивления со стороны продуктовой команды.
Проверка навыков аудита и анализа рисков.
Каким образом вы бы спроектировали систему автоматизированного аудита для обнаружения утечек персональных данных (PII) в тренировочном датасете объемом в несколько терабайт?
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- Страна
- США
- Зарплата
- 320 000 $ – 485 000 $