Software Engineer, Applied AI / Product

Embedding VC; San Francisco (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

Embedding VC in San Francisco is seeking a software engineer to help build an agentic platform at the intersection of medicine and law. You’ll work across the full stack on agent architectures, evals, data contexts, and UI surfaces.

In a typical week you’ll ship new workflows, design eval frameworks, improve context ingestion, and expand tooling for users. You’ll be empowered to own features end-to-end with a 30-year horizon.

Job description

We help physicians to achieve independence by making it easier to start, operate, and grow their practices. We believe that the future of healthcare will not come from bureaucratic health care systems, but individual physicians. We are reducing the barriers for physicians to own their practice and make healthcare better for everyone.

We're a small team obsessed with deep thinking, speed, craftsmanship, and a customer-focused approach, committed to building the best care experience in healthcare with a 30-year horizon.

Our team has built and launched numerous products from 0-to-1, scaled startups from 10 to 300+ employees and from inception to over $1B in valuation, $60M+ ARR, and 100M+ MAU.

We are a group of technologists, physicians, engineers, and designers that are uniquely positioned to design and distribute the product in the healthcare industry.

The Role

We're building an agentic platform at the intersection of medicine and law, transforming complex, high-stakes casework into structured, actionable context. Our system handles patient records, legal workflows, and sensitive decisions, so reliability, clarity, and thoughtful engineering matter deeply.

As a software engineer, you'll work across the full stack: agent architectures, evals, the data and context that feed them, and the surfaces to interact with the platform and its harness.

In a given week, that might mean

• building and shipping a new workflow that takes over a step of the case process, driven by something a physician or our ops team flagged
• designing the eval framework that decides whether an agent's output is reliable enough to ship, while piping the feedback into our synthesized knowledge base
• improving how the platform ingests, indexes, and assembles the right case context for an agent to work from
• adding new tooling to the interfaces users interact with day to day, bringing more of the platform's context and agent capabilities into their workflow
• iterating quickly on a feature with direct input from users, refining the agents based on real-world usage and feedback
• setting the patterns and tooling for AI-native development that the rest of the team builds on

You’ll be a great fit if you

• engineer for correctness and reliability, not just functionality
• are excited to work with frontier models, and care as much about the evals and guardrails around them as the models themselves.
• ship high-quality code fast, and can take a vague problem to a shipped solution without a spec.
• own problems end-to-end, from the agent logic to the interface a user actually uses.
• care about craft and the gap between something that works and something genuinely good to use.
• want to play long-term games with long-term people. We're building for a 30-year horizon, not a quick exit.

Traction / PMF

• We have strong product-market fit ($10MM+ ARR in under 14 months).
• We are profitable and funded by top-tier VCs in the healthcare vertical.
• We are looking for a generalist to work together to scale to hundreds of customers. You'll fit in if you want to take ownership of a product, codebase, or company from 1 to 100.