SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Tennr is building infrastructure to automate the complex paperwork and workflows that delay patient referrals and provider payments in U.S. healthcare. The company uses proprietary AI models to handle insurance authorization, referral processing, and related administrative tasks that currently overwhelm healthcare providers.
You will be a Senior Backend Engineer on the ML team, responsible for bridging cutting-edge ML research and production-grade systems. Your core mission is designing and building agentic workflows—systems powered by LLMs, vision models, and structured automation—that solve infrastructure, workflow, and UX challenges in healthcare operations.
Key responsibilities include:
- Ship full-stack AI systems end-to-end, from ideation through deployment
- Design resilient systems for model deployment, evaluation, and monitoring that scale reliably as traffic grows
- Build data pipelines, evaluation harnesses, and production-grade agentic orchestration systems
- Iterate quickly on experiments and data, moving from concept to code within hours
- Collaborate closely with ML engineers, backend engineers, and cross-functional teams to integrate models with data pipelines and product surfaces
- Research and select the right tools and models for each workflow, ensuring best-fit technology choices
- Work across the full stack: data collection, training pipelines, AI agent orchestration, and frontend integration
You'll work in the Chelsea office 4 days per week, joining a high-caliber team focused on reducing patient delays across U.S. healthcare.
QUALIFICATIONS:
- Proficiency in full-stack development with strong understanding of system design, backend engineering, and observability infrastructure
- Track record of working through the complete lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures
- Hands-on expertise with LLM APIs and proven track record of deploying AI systems into production
- Excellent communication and collaboration skills; ability to align on interfaces, navigate tradeoffs, and drive cross-team execution
