Senior Software Engineer, Applied AI
Post Acute Analytics · Remote ·
- Work mode
- Remote
- Seniority
- Senior
- Category
- AI engineering
At Post Acute Analytics, we have developed a state-of-the-art solution for resolving healthcare's biggest
challenges, and we are on path to become the number one solution for supporting patient journeys,
creating utilization management efficiencies, and helping providers and payors deliver the highest quality
of care.
We are seeking a Senior Software Engineer to join our Data Science team and take ownership of one of
the clinical AI models at the core of our platform. Our team builds production systems that use large
language models (LLMs) to read clinical documentation and support clinical decision making. You will own
one of those systems end to end — its accuracy, its releases, and its behavior in production. This role sits
at the intersection of production software engineering and applied AI with high standards for both.
What You Will Do
- Own a production clinical AI model end to end: iteration, releases, accuracy, and production incident response.
- Collaborate with other engineers and clinical team members to design and maintain the evaluation systems that prove model correctness.
- Partner directly with our clinical team on model audits and requirements, translating clinical rules into system behavior and explaining model output to clinical and product stakeholders.
- Evaluate and integrate new foundation models, including open-weight alternatives, balancing accuracy against latency and cost.
- Act as the internal escalation point when a client or clinician reports issues or has questions about a model.
- Maintain the runtime inference infrastructure in partnership with other engineers.
- Collaborate within an Agile team, engaging in sprint planning, daily standups, backlog refinement, code review, and design discussions. Partner with Product, DevOps, InfoSec, and other engineering teams to align development efforts with business goals.
Technical Experience
- 6 or more years of professional software engineering experience.
- Strong Python skills building and maintaining production services and pipelines.
- Demonstrated experience designing evaluations for AI systems.
- Practical depth in prompt and context engineering.
- Experience with Docker and Kubernetes.
- Experience with PostgreSQL or similar relational databases.
- Experience with event-driven architecture patterns (Kafka or similar).