Senior Backend Engineer (Machine Learning)

• We’re looking for a high-agency Senior Backend Engineer to bridge the gap between cutting-edge ML research and real-world product delivery. You’ll design and build agentic workflows that power complex healthcare processes to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare
• This role involves creating data pipelines, evaluation harnesses, and scalable production-grade agentic systems. You’ll be responsible for researching the right tools and models, ensuring we always use the best-fit technology for each workflow.
• As part of our ML team, you’ll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience, hands-on expertise with LLM APIs, and a proven track record of deploying AI systems into production. You’ll work across the stack - from data collection to training pipelines to AI agent orchestration to frontend integration - bringing cutting-edge AI solutions to life in real-world healthcare environments
• As a Senior Backend Engineer on the ML team at Tennr, you’ll bridge the gap between cutting-edge ML research and real-world product delivery. You’ll help build agentic systems that use LLMs, and vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare
• Ship full-stack AI systems end-to-end
• Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows
• Go from ideation to code within hours; iterate quickly on experiments and data
• Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and product

Benefits

• Meaningful equity
• In-person culture
• Promoting from within
• 401k matching
• Unlimited PTO
• Free lunch (and a pantry stocked with a strangely wide array of puffed corn snacks)

• Excellent communication and collaboration skills: aligning on interfaces, navigating tradeoffs, and driving cross-team execution
• Proficiency in full-stack development, with a strong understanding of system design, backend engineering, and observability infrastructure
• A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures