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# Senior Backend Engineer (ML)

- **Company:** Tennr Incorporated
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Information Engineering, Distributed Systems, Python (Programming Language), PostgreSQL, Machine Learning, TypeScript, Data Logging, Backend, Kubernetes, Machine Learning Operations, TensorRT, Data Pipelines
- **Published:** July 30, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e98d697a5f213d4e

## About the Role

We're looking for a founding Sr. ML Infrastructure Engineer with a strong background in distributed systems and building pipelines that scale. In this role, you'll own the infrastructure that powers Tennr's AI-driven healthcare platform - the training, inference, and data pipelines that let our models handle growing traffic and an expanding product surface., * 4+ years building and scaling infrastructure in production-distributed systems, cloud platforms, or data engineering.

 * Strong backend software engineering fundamentals, with proficiency in Python and TypeScript.
 * Hands-on experience with AWS, and PostgreSQL
 * Solid grasp of observability, reliability, and production incident response.
 * Comfortable with ambiguity and high ownership; you move fast and drive projects from idea to production in a startup environment.
 * Interested in growing into ML infrastructure - prior ML ops/infra experience is not required.
 * Nice to have: exposure to inference engines (vLLM, SGLang, TensorRT), or k8s.

## Description

* Architect, build, and scale the cloud infrastructure behind our ML training, inference, and data pipelines.
 * Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows.
 * Own observability across the stack - logging, metrics, tracing, and alerting.
 * Troubleshoot production issues and continuously improve performance and efficiency.
 * Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and products.


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