- Страна
- США
- Зарплата
- 176 400 $ – 242 550 $
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Reinforcement Learning Infrastructure (Cybersecurity)
Уникальная позиция на острие AI и Cybersecurity в известной компании. Высокая заработная плата, полностью удаленный формат работы в США и возможность работать с ведущими ИИ-лабораториями (OpenAI, Anthropic) делают эту вакансию крайне привлекательной.
Сложность вакансии
Роль требует редкого сочетания глубоких знаний в системном программировании (C/Rust), кибербезопасности (бинарная эксплуатация) и инфраструктуре для обучения с подкреплением (RL). Уровень Staff подразумевает высокую автономность и ответственность за архитектуру сложных конвейеров данных.
Анализ зарплаты
Предлагаемый диапазон ($176k - $242k) полностью соответствует рыночным стандартам для позиции Staff Engineer в США, особенно в таких высокооплачиваемых нишах, как AI Infrastructure и Cybersecurity. Нижняя планка выше медианы для обычных инженеров, что отражает дефицитность навыков.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Reinforcement Learning Infrastructure Engineer position at Bugcrowd. With a deep background in systems engineering and a passion for the intersection of cybersecurity and artificial intelligence, I am excited by the prospect of building the infrastructure that will train the next generation of frontier AI models. My experience in developing high-performance pipelines and reproducible Linux environments aligns perfectly with your mission to automate vulnerability research at scale.
Throughout my career, I have focused on creating robust systems that bridge the gap between complex software analysis and scalable machine learning workflows. I am particularly drawn to Bugcrowd's unique approach of using the Mayhem platform to generate RL environments. My proficiency in Python, C, and Rust, combined with a solid understanding of binary exploitation and fuzzing, positions me to contribute immediately to the RL and Reasoning Team's ambitious goals. I look forward to the possibility of discussing how my technical expertise can help Bugcrowd stay ahead of evolving threats.
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Описание вакансии
We are Bugcrowd. Since 2012, we’ve been empowering organizations to take back control and stay ahead of threat actors by uniting the collective ingenuity and expertise of our customers and trusted alliance of elite hackers, with our patented data and AI-powered Security Knowledge Platform™. Our network of hackers brings diverse expertise to uncover hidden weaknesses, adapting swiftly to evolving threats, even against zero-day exploits. With unmatched scalability and adaptability, our data and AI-driven CrowdMatch™ technology in our platform finds the perfect talent for your unique fight. We aim to create a new era of modern crowdsourced security that outpaces threat actors. Unleash the ingenuity of the hacker community with Bugcrowd, visit www.bugcrowd.com. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others.
Job Summary
The Bugcrowd RL and Reasoning Team focuses on pushing the boundaries of autonomous cybersecurity by building authentic reinforcement learning environments for foundational model companies. As a Staff Engineer, you will advance the frontier of AI Reinforcement Learning development and delivery. You will build the infrastructure and tooling that transforms real-world vulnerability research into large-scale reinforcement learning environments used to train next-generation AI systems.
This role is unique. You will help create the training environments that teach AI systems how to hack and defend software. Your work will directly influence the capabilities of the next generation of AI models. Instead of building a single application, you will build the infrastructure that generates thousands of environments used to train frontier AI systems.
Our team works at the intersection of AI, security research, and systems engineering, building environments that allow models to learn skills such as vulnerability discovery, exploitation, and remediation.
Essential Duties and Responsibilities
If you enjoy building high-performance systems that power cutting-edge AI research, this role is for you.
This role focuses on building the systems that generate RL environments, not just the environments themselves. You will design pipelines that ingest software projects, analyze them with Bugcrowd’s Mayhem platform, and automatically construct training environments used by frontier AI labs including Anthropic, OpenAI, and Cohere.
The ideal candidate is a strong systems engineer who understands:
- Reinforcement learning workflows
- Building clean, reproducible Linux ML environments (containers, MCP, etc)
- System security background in binary exploitation, such as buffer overflows, fuzzing, exploitation, and x86/64.
- Experience developing applications in Python and C, with Rust a plus.
Education, Experience, Knowledge, Skills, and Abilities
Understanding of RL training workflows used by modern LLM systems
- Experience with DevOps pipelines (e.g., github actions), reproducible builds (docker, buildkit, nix).
- Proficiency in Python and C. Other languages (especially Rust) are a plus.
- Understanding of software vulnerabilities, fuzzing, or program analysis
- Experience with build systems and large open-source codebases
- Comfort working with Linux systems and low-level debugging
- Experience working with benchmark environments (CTFs, SWE-bench, security challenges, etc.) is a plus
Working Conditions and Physical Requirements
The ideal candidate must be able to complete all physical requirements of the job with or without reasonable accommodation.
Sitting and / or standing - Must be able to remain in a stationary position 50% of the time
Carrying and / or lifting - Must be able to carry / move laptop as needed throughout the work day.
Environment - remote, work-from-home 100% of the time.
ADA Statement:
Bugcrowd is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Bugcrowd will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please contact HR at ada@bugcrowd.com.
Pay Range Disclosure
At Bugcrowd, we strive for fairness, equality and to create an environment that allows our people to perform at their very best. Our compensation philosophy is to foster a collaborative community that rewards, attracts and retains the best possible talent. The provided salary details are based on US national averages and we retain the flexibility to tailor to the needs of the business.
The national estimate for the current base range for the position of $176,400 - $242,550.
This position may also be eligible to participate in a discretionary bonus program or commission plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Culture
- At Bugcrowd, we understand that diversity in the workplace is vital to a company’s success and growth. We strive to make sure that people are included and have a sense of being part of making Bugcrowd not only a great product but a great place to work.
- We regularly hear from both customers and researchers that Bugcrowd feels like a family, and we strive to maintain that internally as well.
- Our team consists of a broad range of people: musicians, adventure sports junkies, nature lovers, parents, cereal enthusiasts, night owls, cyclists, artists—you get the point.
At Bugcrowd, we are solving security threats and vulnerabilities that are relevant to everyone, therefore we believe solving these problems takes all kinds of backgrounds. We value the perspectives and experiences people from underrepresented backgrounds bring.
Disclaimer
This position has access to highly confidential, sensitive information relating to the technologies of Bugcrowd. It is essential that the applicant possess the requisite integrity to maintain the information in the strictest confidence.
The company is authorized to obtain background checks for employment purposes under state and federal law. Background checks will be conducted for positions that involve access to confidential or proprietary information (including trade secrets).
Background checks may include Social Security verification, prior employment verification, personal and professional references, educational verification, and criminal history. Applicants with conviction histories will not be excluded from consideration to the extent required bylaw.
Equal Employment Opportunity:
Bugcrowd is EOE, Disability/Age Employer.
Individuals seeking employment at Bugcrowd are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.
Bugcrowd is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Bugcrowd will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please contact HR at ADA at bugcrowd.com.
Apply at: https://www.bugcrowd.com/about/careers/
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Навыки
- Cybersecurity
- C++
- Python
- Rust
- Linux
- GitHub Actions
- DevOps
- Docker
- Reinforcement Learning
- Nix
- Fuzzing
- Buildkit
- Binary Exploitation
Возможные вопросы на собеседовании
Вакансия требует понимания того, как ИИ обучается взлому и защите ПО.
Как бы вы спроектировали масштабируемую среду для RL, которая имитирует реальную уязвимость в бинарном файле, обеспечивая при этом детерминизм и воспроизводимость?
Упоминается использование Docker, Buildkit и Nix для создания сред.
В чем преимущества использования Nix перед традиционными Docker-контейнерами при создании воспроизводимых сред для обучения ML-моделей?
Роль включает работу с низкоуровневым кодом и уязвимостями.
Опишите ваш опыт работы с фаззингом или инструментами анализа программ (например, Mayhem или AFL). Как результаты этих инструментов можно интегрировать в цикл обучения агента RL?
Необходимо понимание рабочих процессов RL.
С какими основными проблемами производительности вы сталкивались при передаче данных между высокопроизводительной средой на C/Rust и фреймворком обучения на Python?
Вакансия Staff-уровня предполагает системное мышление.
Как обеспечить безопасность хост-системы при автоматическом запуске и анализе тысяч потенциально уязвимых или вредоносных программ в тренировочных средах?
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- Страна
- США
- Зарплата
- 176 400 $ – 242 550 $