AI Product Engineer

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

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.

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.

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  Rapidly prototype and ship AI-powered solutions to validate ideas and drive real impact

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  Own problems end-to-end from ambiguous idea to production system with minimal oversight

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  Build AI-powered products and tools that automate workflows, support decision making, and improve internal and customer-facing experiences

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  Operate across the stack including backend services, APIs, data pipelines, and lightweight product surfaces

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  Integrate AI capabilities such as LLMs into real workflows, ensuring they are reliable, observable, and cost-efficient

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  Run fast feedback loops by testing, measuring, and iterating using evaluation frameworks and real usage data

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  Develop reusable patterns and tooling that enable faster experimentation and iteration across the team

• 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

• 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