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# ML & Cloud Infrastructure Engineer

- **Company:** Gritt Robotics Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Python (Programming Language), Tensorflow, Parquet, Data Ingestion, Pytorch, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines, Docker
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/ml-cloud-infrastructure-engineer-gritt-robotics-inc-8129738

## About the Role

* Degree in computer science or related engineering disciplines (or equivalent experience).

 * 4+ years of experience deploying high-performance ML pipelines in production.
 * Proficient in Python and comfortable with C++/Go.
 * Experience with ML frameworks like PyTorch.
 * Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc.
 * Experience with deploying on cloud platforms like AWS, GCP or Azure.
 * Experience with tooling like Docker, Kubernetes, and Airflow.

 * Should be comfortable taking ownership of tasks with light supervision.

 * Must have excellent problem-solving skills.

 * Legally authorized to work in the United States.

## Description

We're looking for an experienced ML & Cloud Infrastructure Engineer to join our team. As an early member, you will play a pivotal role in architecting scalable cloud infrastructure for our AI and data pipelines. You'll need to thrive in a fast-paced startup environment where you'll wear multiple hats and have a direct impact on our product's evolution. Ideally, you have a proven track record of developing and deploying high-performance ML and cloud pipelines in production, and you're passionate about pushing the boundaries of what's possible in robotics with AI.

What you'll get to work on

 * Develop and deploy scalable AI training and validation pipelines in the cloud.

 * Spin up distributed pipelines for data ingestion, pre-processing, training and evaluation.
 * Deploy monitoring and CI/CD pipelines.
 * Enable large-scale evaluation of AI models via cloud-based metrics.
 * Enable large-scale evaluation of autonomy software and models via simulations in the cloud.
 * Optimize performance, I/O and GPU utilization.
 * Build tooling and dashboards for rapid experimentation, orchestration and visualization.
 * Work with other teams to integrate cloud tooling into workflows.


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