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AI Sim - Staff ML Research Engineer

Оценка ИИ

Исключительная возможность работать в топовом стартапе (выходце из Alphabet) над социально значимыми задачами. Высокий уровень компенсации, работа с передовым стеком технологий и сильная команда экспертов.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует редкого сочетания глубоких знаний в области физики/химии, экспертного владения распределенным обучением ML-моделей и навыков разработки ПО промышленного уровня. Уровень Staff подразумевает высокую степень автономности и ответственности за архитектуру.

Анализ зарплаты

Медиана230 000 $
Рынок190 000 $ – 280 000 $
Оценка ИИ

Учитывая уровень Staff и специфику AI/Quantum в США, рыночная зарплата для таких позиций значительно выше средней по рынку разработки. SandboxAQ, как компания с венчурным капиталом и корнями в Alphabet, обычно предлагает конкурентные пакеты, включающие значительную долю опционов.

Сопроводительное письмо

I am writing to express my strong interest in the Staff ML Research Engineer position at SandboxAQ. With over five years of experience in developing production-grade software and a deep background in distributed ML training, I am excited by the opportunity to bridge the gap between visionary research and scalable products in the AI Simulation team. My expertise in optimizing GPU-accelerated pipelines and my passion for Large Quantitative Models align perfectly with your mission to revolutionize drug discovery.

In my previous roles, I have successfully transitioned high-impact prototypes into robust, external-facing products, often working within interdisciplinary teams where physics and AI intersect. I am particularly drawn to SandboxAQ's unique position as an Alphabet spin-off and your commitment to tackling epic challenges through multidisciplinary collaboration. I am confident that my technical leadership and experience with agentic coding tools will contribute significantly to architecting the next generation of your scientific codebases.

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Описание вакансии

About SandboxAQ

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.

We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.

At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

The Opportunity

The AI Sim R&D team creates leading edge ML and physics-based models ("LQMs") to advance drug and materials discovery. We are a flexible, creative, and impact driven team of multidisciplinary scientists and engineers, whose products dramatically accelerate the creation of molecules and medicines.

As a Staff ML Research Engineer, you will be the bridge between visionary research and production-grade reality, tasked with making in silico design the dominant paradigm in drug discovery. Your central purpose is to architect, scale, and optimize the scientific codebases that power our LQMs. Over your first year, you will drive the transition of high-impact prototypes into robust products, orchestrate distributed training pipelines on world-class GPU infrastructure, and pioneer hardware-level optimizations that push the boundaries of computational chemistry.

Key Responsibilities

  • Architect and Scale: Bring content of scientific papers into promising, scalable ML algorithms; and translate these into high-performing and robust scientific code
  • ML Engineering: Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks.
  • GPU Expertise: Implement advanced software and hardware optimizations to maximize the efficiency of ML pipelines across distributed cloud GPU environments.
  • Ownership of the Lifecycle: Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage.

Essential Skills & Experience

  • Academic Foundation: MSc (PhD preferred) in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods.
  • Software Excellence: Senior (5+ years) industry experience developing productionized software in professional teams.
  • Distributed Systems: Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs (e.g. PyTorch, TensorFlow).
  • Agentic Coding: Deep familiarity with agentic coding tools (e.g. Claude code, Codex).
  • Product Lifecycle Mastery: Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between "research code" and "product code".

Highly Desired Skills & Experience

  • Domain Expertise: Direct experience in biopharma or training leading-edge affinity, structure-prediction, or generative chemistry models.
  • Commercial Insight: A history of developing and launching successful commercial software products within a professional engineering team.
  • Advanced MLOps: Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring.
  • Collaborative Innovation: Experience working in interdisciplinary environments where AI intersects with physical or biological sciences.

Why Join Us?

We offer a comprehensive and competitive benefits package designed to support your health, financial well-being, and life outside of work.

  • Compensation: Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
  • Benefits: Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions, retirement savings with company matching, paid parental leave, and inclusive family-building benefits.
  • Work-Life Balance: Flexible paid time off, company-wide seasonal breaks, and support for flexible work arrangements that enable sustainable performance.
  • Career Development: Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.

SandboxAQ Welcomes All

We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.

Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.

Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.

Read: Guidance for candidates on using AI Tools in interviews

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Навыки

  • Python
  • PyTorch
  • Machine Learning
  • MLOps
  • Computational Chemistry
  • Distributed Systems
  • TensorFlow
  • Claude Code
  • GPU Optimization
  • Codex

Возможные вопросы на собеседовании

Проверка опыта работы с высоконагруженными вычислениями, критически важными для LQMs.

Расскажите о вашем опыте оптимизации распределенного обучения моделей на больших кластерах GPU. С какими узкими местами вы сталкивались?

Оценка способности кандидата превращать научные идеи в работающий продукт.

Как вы подходите к процессу перевода теоретических алгоритмов из научных статей в масштабируемый и поддерживаемый код?

Проверка владения современными инструментами разработки, указанными в вакансии.

Как вы интегрируете инструменты 'agentic coding' (например, Claude code) в свой рабочий процесс для повышения эффективности разработки?

Оценка опыта работы в междисциплинарных командах.

Опишите случай, когда вам приходилось тесно сотрудничать с учеными (биологами или химиками) для решения технической задачи. Как вы находили общий язык?

Проверка навыков архитектурного проектирования.

Как бы вы спроектировали MLOps пайплайн для модели, которая должна постоянно дообучаться на новых данных химических симуляций?

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