Software Engineer, Applied AI

About the role

Embedding Vc is building an agent-driven platform that streamlines intricate medical and legal processes, converting complex case information into structured, actionable insights. This system manages patient records, legal procedures, and critical decisions, emphasizing the need for robust reliability and clear engineering. You will own the core logic that transforms unstructured text into reliable, queryable data representations for downstream agents. The role requires you to design and implement the pipelines that ensure data integrity from raw ingestion to final retrieval. You will collaborate closely with product and clinical or legal experts to refine the behavior of these systems in production. This position is for a generalist who is comfortable bridging the gap between AI capabilities and production software standards. You will have a direct impact on the architecture that determines how customers interact with sensitive case data. The work demands a balance of rigorous engineering practices and the flexibility to adapt to rapidly evolving requirements in a high-stakes domain.

Key facts

• Interested in long-term projects and collaborations, building for a 30-year outlook.

What you'll do

Design and implement scalable data ingestion workflows that handle complex medical and legal documents with varying formats and quality.

Build and maintain evaluation frameworks that measure the accuracy and consistency of structured data extracted by AI agents.

Develop user interface components that allow operators to inspect, correct, and validate the transformations applied to source material.

Implement agent orchestration patterns that ensure reliable execution of multi-step reasoning tasks across large case files.

Optimize vector indexing strategies to support fast retrieval of relevant information within massive document collections.

Partner with domain experts to translate ambiguous procedural requirements into concrete software specifications and acceptance criteria.

Create monitoring and alerting systems that surface anomalies in data quality or agent behavior as soon as they occur.

Contribute to the architectural decisions that define the boundaries between AI components and traditional software services.

Own the testing strategy for new features, ensuring that changes do not degrade performance or introduce regressions in critical workflows.

Help define the product roadmap by providing technical insights on the feasibility and trade-offs of new capabilities.

Scale the backend infrastructure to support hundreds of customers without sacrificing performance or data isolation guarantees.

Write clean, maintainable code that serves as documentation for future engineers working on the system.

Participate in code reviews to uphold high standards for security, performance, and clarity in the implementation.

Act as a technical liaison between the engineering team and the healthcare or legal customers who rely on the platform.

Requirements

You have a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

You possess 5 or more years of professional software development experience shipping products to production.

You are proficient in at least one modern statically typed programming language such as Rust, Go, or TypeScript.

You have hands-on experience building and deploying AI applications or working with large language models in a production environment.

You understand the fundamentals of data structures, algorithms, and system design at a level that allows you to architect scalable services.

You have worked with database systems, both SQL and NoSQL, and you understand the trade-offs involved in choosing a storage solution.

You are comfortable debugging complex issues in distributed systems where failures can be intermittent and hard to reproduce.

You have a strong grasp of software testing principles and practice writing unit, integration, and end-to-end tests.

Nice to have

Experience with retrieval-augmented generation pipelines and vector databases.

Familiarity with compliance frameworks relevant to healthcare or legal data.

Contributions to open-source projects that involve agent frameworks or evaluation tools.

Background in computational linguistics or information retrieval.

Practical notes

Embedding Vc has achieved over $10MM ARR in under 14 months and is profitable, backed by leading healthcare VCs. The company is seeking a generalist to help scale to hundreds of customers, offering significant ownership in product, codebase, or company growth from early stages.

Interested candidates should relocate to the San Francisco Bay Area to be considered for the role.

The position is full-time and requires a commitment to the long-term vision of the company.

No specific deadlines for applications are published; candidates are encouraged to apply as soon as possible.