Product Engineer

We are currently hiring for a Healthtech AI client in SF who are building agentic health infrastructure for clinical operations and are looking to scale their engineering team after raising $50m in funding!They are looking for AI product engineers who can own features end to end and take a high level of ownership. This company do not have any PMs within the business, so engineers own product decisions in close collaboration with the founders.Responsibilities:Produce high-quality, maintainable, and scalable technical work, with a strong focus on reliability, scalability, and readability.Take full ownership of product features, from ideation and design to implementation, testing, and deployment.Show initiative by staying current on emerging technologies and suggesting improvements to our stack and processes.Demonstrate reliability through proactive communication, clear timelines, and the ability to unblock yourself and others when needed.Spend time understanding and developing a deep intuition of the product that you're building.Collaborate across disciplines - with clinicians, end users, and non-technical members of the team - by translating goals into clear, actionable technical solutions.Collaborate within engineering through active ideation, thoughtful code reviews, and helping teammates design and deliver robust solutions.Requirements:4+ years of full-time software engineering experienceBachelor's or advanced degree in Computer Science or a related fieldStrong ability to learn and adapt to new languages, frameworks, and technologiesProficiency in Python is importantExcellent problem-solving, analytical, and communication skillsA genuine interest in applying technology to improve healthcareComfort using AI coding tools - Cursor, Claude etc.Nice to Have:Familiarity with infrastructure tooling: Kubernetes, Helm, Terraform, AWS/GCPFamiliarity with frontend languages and frameworks - React/TypescriptWillingness to interface with end users and assist with use case discoveryComfort interacting with LLM tooling and willingness to learn about agentic workflows