/roles — ROLE_220
Senior Software Engineer, Applied AI
Series A healthcare AI company whose conversational agents handle hiring for hospitals and care providers
The role
- COMP
- $190K - $240K
- EQUITY
- Competitive equity
- LOCATION
- New York
- WORKPLACE
- Hybrid
- EXPERIENCE
- 6+ years
- VISA
- None
- STACK
- Python, JavaScript, TypeScript, NodeJS, AWS, GCP, Azure, Docker, Kubernetes, Prometheus, Grafana
- INDUSTRY
- Software Development, AI, Healthcare
The company
Healthcare AI company whose autonomous agents run clinical hiring for health systems, handling every step between opening a role and making an offer.
- STAGE
- Series A-stage
- FUNDING
- $10M+ raised
- TEAM
- ~20 people
- FOUNDED
- 2021
- BACKING
- VC-backed
JD — the work
About the role
This role designs, builds, and scales the AI systems behind an autonomous recruiting platform that helps hospitals and other care providers fill clinical roles faster. You would own applied AI work end to end, from prototypes and model selection to production reliability, as conversation volume grows by orders of magnitude. The team is lean and fast-moving, and the role suits a hands-on engineer who has shipped agentic, multi-agent, and RAG systems in production. The team works hybrid in New York.
What you'll do
- Build agent workflows and AI features, including retrieval and agent-to-agent coordination, and put them into production
- Choose and tune the models in the voice stack (speech-to-text, text-to-speech, voice activity detection), balancing cost, speed, reliability, and quality
- Create evals, guardrails, and monitoring that keep agent behavior measurable and safe as usage grows
- Run the backend and the data and inference pipelines serving these features in production
- Work with the product team and founding engineers to prototype and ship fast, shaping technical direction
What they're looking for
- 6+ years of software engineering with hands-on applied AI work
- Production experience with agentic AI, including multi-agent systems and RAG
- Experience choosing, evaluating, and tuning models against latency, cost, and quality targets
- Strong backend skills in Python and TypeScript/Node, with cloud (AWS, GCP, or Azure), Docker, and Kubernetes
- Comfort with heavy ownership at a quick-moving early company