# Machine-Learning Operations Engineer

**Company:** [Tennr](https://hotfix.jobs/companies/tennr)
**Location:** New York, NY
**Role:** ML Engineering
**Salary:** $200k – $230k/yr
**Experience:** 5+ years
**Skills:** Python, TypeScript, PyTorch, TensorFlow, vLLM, TensorRT, Triton, CUDA, Ml Ci/Cd, Gpu Orchestration
**Posted:** 2026-07-08

> Founding ML Operations Engineer building scalable training, inference, and evaluation pipelines for proprietary VLMs and LLMs in healthcare. Requires 5+ years production ML infrastructure experience, strong Python/TypeScript skills, and ownership in a fast-paced startup.

## Job Description

## 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.

## Requirements
- 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-Haves
- Experience working with ML CI/CD and common ML frameworks like PyTorch, TensorFlow, etc.
- Experience working with common inference frameworks like vLLM, TensorRT, Triton, etc.
- Experience with GPU orchestration, including managing GPU workloads/scheduling, cost management, cluster utilization, etc.
- Experience with GPU optimization (training/inference) involving CUDA profiling, memory optimization, multi-GPU communication, etc.

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