Software Engineer - AI Platform

Truveta•Seattle, WA

60d•$94,000 - $120,000•Hybrid

About The Position

Truveta is the world's first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta' s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values. This position is based out of our headquarters in the Greater Seattle area. #LI-hybrid Who We Need Truveta is rapidly building a talented and diverse team to tackle complex challenges at the intersection of AI, data, and healthcare. Beyond technical ability, we're looking for builders: problem solvers, collaborators, and innovators who thrive on turning great ideas into scalable, reliable systems. If you're motivated by mission-driven work, eager to shape foundational AI infrastructure, and excited to empower others to contribute effectively to a large, evolving codebase, we'd love to meet you. This Opportunity We are seeking a Software Engineer focused on AI Platform Infrastructure to build and scale the engineering frameworks, developer tools, and shared libraries that power Truveta's next-generation AI experiences and end-to-end features. This role is central to enabling our teams to deliver reliable, high-performing, and impactful AI-driven products. You will work deeply within the core architecture and coding infrastructure that underpins Truveta's AI ecosystem - shaping how engineers design, build, and deploy AI-powered functionality across our platform. Beyond improving developer velocity and system performance, you'll contribute directly to the creation of intelligent, user-facing experiences that make our technology more capable and intuitive.

Requirements

BS degree in Computer Science or a related technical field, or equivalent practical experience through internships, coursework, or projects
1+ years of hands-on programming experience in professional, academic, or open-source environments
1+ years developing and maintaining Python-based web services (FastAPI experience a plus)
Experience with cloud platforms such as Azure, AWS, or GCP; Azure experience strongly preferred
Familiarity with containerized application development (Docker or Kubernetes)
Solid understanding of software engineering fundamentals - data structures, debugging, testing, and version control (Git)
Exposure to modern development workflows such as code reviews, CI/CD pipelines, and collaborative development tools
Interest in system architecture, scalability, and developer tooling, with curiosity about how infrastructure design influences performance
Strong communication and teamwork skills, with a pragmatic and collaborative approach to problem solving
Growth-oriented mindset - eager to learn, adapt, and contribute to evolving technical areas

Nice To Haves

Experience with MCP (Model Context Protocol) architecture, AI systems or APIs, or large-scale codebases is preferred.

Responsibilities

Design, implement, and maintain core libraries, SDKs, and reusable modules that enable fast, consistent development across our AI services.
Help establish engineering standards, guidelines, and best practices for scalable and maintainable code.
Build and evolve developer frameworks that support efficient AI model integration, prompt orchestration, and real-time user interactions.
Collaborate with product, research, and ML engineering teams to deliver infrastructure that improves both system performance and developer experience.
Participate in code reviews and design discussions to build consensus on best practices and identify opportunities for improvement.
Work closely with teammates to build alignment and adoption for new tools, frameworks, and patterns that raise our engineering quality bar.
Enhance observability, logging, and telemetry across the AI stack to support rapid iteration and issue resolution.
Continue learning new technologies, frameworks, and practices to keep our infrastructure modern and effective.
Commit to ongoing training in areas like privacy, ethics, and security to uphold user trust and product integrity