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AI Product Engineer

Оценка ИИ

Отличная вакансия в быстрорастущем стартапе с серьезным финансированием ($50M) и благородной миссией. Предлагает высокую степень свободы, работу с передовым стеком (LLM, LangGraph) и отличный пакет льгот, включая поддержку ментального здоровья.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует высокого уровня автономности и умения работать на стыке разработки продукта, данных и инфраструктуры. Основная сложность заключается в необходимости быстрого прототипирования и внедрения LLM в реальные медицинские рабочие процессы с соблюдением стандартов надежности.

Анализ зарплаты

Медиана175 000 $
Рынок140 000 $ – 220 000 $
Оценка ИИ

Зарплата в вакансии не указана, но для позиции AI Product Engineer в Нью-Йорке рыночные показатели очень высоки. Учитывая стадию финансирования компании (Series B/C), можно ожидать конкурентоспособное предложение на уровне или выше среднего по рынку, дополненное опционами.

Сопроводительное письмо

I am writing to express my interest in the AI Product Engineer position at Camber. With over 3 years of experience in building production-grade backend systems and a strong proficiency in Python, I am excited about the opportunity to apply my skills to streamline healthcare workflows. Your mission to improve the accessibility of care through rapid AI experimentation resonates deeply with my background as a generalist builder who thrives in autonomous, fast-paced environments.

In my previous roles, I have focused on owning problems end-to-end, from ambiguous ideas to scalable production systems. I am particularly impressed by Camber's commitment to building reliable and observable AI capabilities. Having worked with LLM integration and evaluation frameworks, I am confident in my ability to contribute to your team's efforts in developing reusable patterns for AI experimentation and ensuring cost-efficient, high-impact solutions for clinicians.

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Присоединяйтесь к Camber, чтобы создавать ИИ-решения, которые реально меняют жизни пациентов и работу врачей!

Описание вакансии

About Us:

Camber builds software to improve the quality and accessibility of healthcare. We streamline and replace manual work so clinicians can focus on what they do best: providing great care. For more details on our thesis, check out our write-up: What is Camber?

We’ve raised $50M in funding from phenomenal supporters at a16z, Craft Ventures, YCombinator, Manresa, and many others who are committed to improving the accessibility of care. For more information, take a look at: Announcing Camber

About our Culture:

Our mission to change behavioral health starts with us and how we operate. We don’t want to just change behavioral health, we want to change the way startups operate. Here are a few tactical examples:

1) Improving accessibility and quality of healthcare is something we live and breathe. Everyone on Camber’s team cares deeply about helping clinicians and patients.

2) We have to have a sense of humor. Healthcare is so broken, it's depressing if you don't laugh with us.

About the role:

As an AI Product Engineer at Camber, you’ll work on building AI-powered products and internal capabilities that improve how we operate and deliver value to customers. This role is centered on fast experimentation, rapid iteration, and owning problems end to end with freedom to build.

We’re looking for someone who is highly autonomous and excited to help shape our AI direction, define what we build, and establish how we build it. You’ll play a foundational role in determining how AI is applied across our products and internal workflows.

What you'll do:

  • Rapidly prototype and ship AI-powered solutions to validate ideas and drive real impact
  • Own problems end-to-end from ambiguous idea to production system with minimal oversight
  • Build AI-powered products and tools that automate workflows, support decision making, and improve internal and customer-facing experiences
  • Operate across the stack including backend services, APIs, data pipelines, and lightweight product surfaces
  • Integrate AI capabilities such as LLMs into real workflows, ensuring they are reliable, observable, and cost-efficient
  • Run fast feedback loops by testing, measuring, and iterating using evaluation frameworks and real usage data
  • Develop reusable patterns and tooling that enable faster experimentation and iteration across the team

What we're looking for:

  • Experience: 3+ years of experience building production systems as a backend or full-stack engineer
  • Strong Python proficiency: comfortable building production systems and data workflows in Python
  • Highly autonomous: able to take loosely defined problems and independently drive them to outcomes
  • Strong experimentation and analytical approach: able to design experiments, construct evaluation frameworks using historical data, and iterate quickly based on results
  • Generalist builder: comfortable working across product engineering, data, and infrastructure, with a focus on shipping practical solutions rather than research or theory
  • Customer and product intuition: able to understand workflows and pain points to build useful systems
  • Curiosity: motivated to learn new tools, frameworks, and domains quickly, especially in a fast-evolving AI landscape

Nice to have:

  • Experience integrating LLMs or AI capabilities into production systems
  • Familiarity with evaluation tools such as Arize Phoenix or Promptfoo
  • Experience building agent-based workflows using frameworks like LangGraph
  • Familiarity with orchestration tools such as Temporal
  • Exposure to AI observability and tracing standards such as OpenInference
  • Familiarity with cloud platforms such as AWS

Perks and Benefits at Camber:

  • Comprehensive Health Coverage: Medical, dental, and vision plans with nationwide coverage, including 24/7 virtual urgent care.
  • Mental Health Support: Weekly therapy reimbursement up to $100, so you can prioritize the care that works best for you.
  • Paid Parental Leave: Up to 12 weeks of fully paid time off for new parents (birth, adoption, or foster care).
  • Financial Wellness: 401K (traditional & Roth), HSA & FSA options, and monthly commuter benefits for NYC employees.
  • Time Off That Counts: We offer flexible PTO, meaning you’re trusted to take the time you need to recharge while balancing your responsibilities and team commitments.
  • Fitness Stipend: $100/month to use on fitness however you choose.
  • Hybrid Flexibility: In NYC? We work in the office 3–5x/week, with flexibility when life happens. Fridays are remote-friendly.

Camber is based in New York City, and we prioritize in-person and hybrid candidates. Please refer to the top of this page for this position's hiring location.

Building an inclusive culture is one of our core tenets as a company. We’re very aware of structural inequalities that exist, and recognize that underrepresented minorities are less likely to apply for a role if they don’t think they meet all of the requirements. If that’s you and you’re reading this, we’d like to encourage you to apply regardless — we’d love to get to know you and see if there’s a place for you here!

In addition, we take security seriously, and all of our employees contribute to uphold security requirements and maintain compliance with HIPAA security regulations.

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Навыки

  • AWS
  • Python
  • LLM
  • HIPAA
  • Backend Development
  • APIs
  • Observability
  • Data Pipelines
  • Temporal
  • LangGraph
  • Promptfoo
  • Arize Phoenix

Возможные вопросы на собеседовании

Проверка опыта работы с LLM в реальных условиях и понимания стоимости/производительности.

Расскажите о случае, когда вы внедряли LLM в продакшн: с какими проблемами надежности или задержек вы столкнулись и как их решили?

Оценка навыков тестирования и оценки качества ответов ИИ.

Как вы проектируете системы оценки (evaluation frameworks) для проверки качества работы ИИ-агентов перед деплоем?

Проверка автономности и умения работать с неопределенностью.

Опишите ситуацию, когда вам дали очень размытую задачу. Как вы превратили её в работающий продукт с минимальным контролем со стороны?

Оценка технического кругозора в области оркестрации.

В каких случаях вы бы предпочли использовать Temporal для управления рабочими процессами вместо простых скриптов или очередей задач?

Проверка продуктового мышления в контексте здравоохранения.

Как бы вы подошли к автоматизации заполнения медицинских карт с помощью ИИ, учитывая требования HIPAA и необходимость высокой точности?

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