Senior ML Ops Engineer
About the role
Senior ML Ops Engineer responsible for building and scaling ML training/inference pipelines and ensuring robust, scalable model deployment within Tennr’s AI-driven healthcare platform.
Responsibilities
- Architect, design, and implement ML software systems for deploying and managing models at scale.
- Develop and maintain infrastructure that supports efficient ML operations, including data pipelines, model evaluations, deployments, and training at scale.
- Collaborate closely with ML engineers, software engineers, and cross-functional teams to ensure seamless integration of models with data pipelines and products.
- Troubleshoot production issues and continuously improve systems to enhance performance and efficiency.
- Create tooling for online and offline evaluation of ML & LLM systems.
Qualifications
- 5+ years of experience in ML model deployment, infrastructure, and scaling in production environments
- Strong software engineering fundamentals, with proficiency in Python and TypeScript
- Experience in software design and architecture for highly available ML systems for use cases like inference, evaluation, and experimentation
- Strong knowledge of observability, including logging, metrics, tracing, model performance monitoring, and alerting
- Experience with distributed systems, reliability, and production incident response
- Comfortable working in ambiguity with high ownership, moving quickly in a fast-paced startup environment, and proactively driving projects from idea to production
- Nice to have: Experience working with ML CI/CD and common ML frameworks like PyTorch, TensorFlow, etc.; Inference frameworks like vLLM, TensorRT, Triton; GPU orchestration and optimization (CUDA profiling, memory optimization, multi-GPU comms)
Skills
Benefits
- Beautiful new office at 345 Hudson Street
- Unlimited PTO
- 100% paid employee health benefit options
- Employer funded 401(k) match
- Competitive parental leave
About Tennr
Fast, Transparent Patient Experiences
Tennr is solving the black hole problem in healthcare by leveraging models to digitize the flow of paperwork between healthcare providers. -- Today, when you go to your doctor and need to be referred to a specialist (e.g., for sleep apnea), your doctor sends a fax (yes, in 2024, 90% of provider-provider communication is a 1980s fax). These are often converted into 20+ page PDFs, with handwritten (doctor’s handwriting!) notes, in thousands of different formats. The problem is so complex that a person has to read it, type it up, and manually enter your information. Tennr built RaeLM™ (7B—trained on 3M+ documents) to read these docs, talk to your doc to ensure nothing is missed, and text you to help schedule your appointment so you can get better, faster.
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