Datafielder

The Role

We are looking for one Staff Software Engineer to join an early team. This is a full-stack role with a backend lean, ideal for someone who has built end-to-end features, cares about product quality, and is excited to work across infrastructure, application code, and user experience.

As we evolve the platform, we are integrating AI thoughtfully both in how we build and in what we deliver. You will help shape user-facing features where LLMs and automation improve hospital workflows, while also using tools like Cursor, ChatGPT, and Copilot to move faster and build better. We are looking for someone who is not just comfortable with AI in the loop, but curious about how it can reshape the user experience in high-trust domains like healthcare.

What You Will Do

• Build impactful features end-to-end across React, TypeScript, Postgres, and AWS Lambda to improve real workflows for staff and patients.

• Design with intention and create robust, extensible systems built for the realities of clinical environments.

• Shape how AI works for healthcare, from accelerating development internally to streamlining frontline workflows.

• Collaborate on critical trade-offs with product and clinical teams to balance safety, usability, and automation.

• Own the full project arc, from clarifying goals and shaping architecture to managing risk and improving after launch.

• Co-own our engineering culture by raising concerns early, refining processes, and making every release smoother than the last.

What We Are Looking For

This role is a strong match if you have:

• 5 to 8 years of software engineering experience, with an emphasis on backend systems and full-stack delivery.

• Strong, production TypeScript across the stack, including React on the frontend and Node.js on the backend.

• Real depth with relational databases, particularly Postgres, including schema design, indexing, and performance considerations.

• Hands-on AWS experience, including Lambda, Terraform, or other infrastructure-as-code work.

• A track record of shipping AI or LLM-powered features in production, not just experiments or prototypes.

• Daily, fluent use of modern AI coding tools, such as Cursor, GitHub Copilot, ChatGPT, or Claude, as part of how you actually build software.

• Recent experience at an early or fast-moving startup. You have shipped in ambiguous environments without a large support structure.

• A thoughtful, communicative approach to problem-solving. You can explain your reasoning, surface trade-offs, and push back when something feels wrong.

Strongly Preferred

• Previous experience as a founding engineer or one of the first engineering hires at a startup.

• Health tech experience at a modern startup.

• Experience owning an on-call rotation.

• Familiarity with Next.js, Prisma, AWS Fargate, S3, Cognito, or Datadog.

What This Role Is Not

We believe in being direct about expectations. This role may not be the right fit if:

• You are looking for a strict 9-to-5. We are not 996, but this is not a place to coast either. Early-stage ownership means real engagement.

• Your experience is exclusively at legacy or large enterprise software systems without recent startup work.

• You are not interested in being on-call. On-call rotation is part of this role.

• You cannot be in New York or San Francisco. This is not a fully remote position.

• You view AI tools as something to avoid or distrust. We expect daily, fluent use.

Our Tech Stack

React, TypeScript, Node.js, Postgres, Prisma, AWS Lambda, AWS Fargate, S3, Cognito, Terraform, GitHub Actions, Datadog, Appsmith, JIRA, Slack. Daily AI tools: Cursor, ChatGPT, GitHub Copilot.

Compensation and Benefits

• Base salary: $180,000 to $200,000, based on experience.

• Meaningful equity in an early-stage, well-funded company.

• Health, dental, and vision benefits.

• The opportunity to shape product, engineering culture, and how the company scales.

Interview Process

• Initial recruiter screen with DataFielder.

• Technical screen with engineering team.

• Take-home or technical interview, focused on simple, clear product thinking.

• Cultural conversation with the broader team.

• Final round, which may include an in-person visit in New York or San Francisco.