> Markdown version of [/jobs/ext/3683477-ml-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/3683477-ml-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Infrastructure Engineer

- **Company:** Zipline
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $160,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Cloud Computing, Data Infrastructure, Programming Tools, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Software Engineering, Feature Store, Pytorch, Deep Learning, Cloudformation, Kubernetes, Infrastructure Automation Frameworks, Performance Monitor, Data Management, Machine Learning Operations, Terraform, Data Pipelines
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/15_3185a74d1

## About the Role

* 3+ years of professional software engineering experience, ideally including ML infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or another safety-critical hardware/product environment.
 * Strong software engineering practices in Python in a production setting; comfort designing APIs, services, schemas, jobs, and operational workflows.
 * Experience building reproducible data pipelines and machine-learning pipelines.
 * Experience monitoring data statistics, system performance metrics, pipeline failures, and model/evaluation signals.
 * Working knowledge of ML concepts such as datasets, training, evaluation, optimization, statistics, and modern deep learning workflows.
 * Generalist mindset and willingness to work across cloud services, data platforms, developer tooling, and embedded/robotics-adjacent constraints.
 * Experience with PyTorch or similar ML frameworks.
 * Strong ownership, clear communication, and interest in building secure systems for mission-critical workflows.
 * Experience with Kubernetes or other container orchestration systems for production workloads.
 * Experience with cloud and on-premise production infrastructure, preferably AWS, and infrastructure-as-code tools such as Terraform or CloudFormation., * Experience deploying or evaluating ML systems on real robots, autonomous vehicles, drones, or other hardware products.
 * Experience with large-scale training systems, feature stores, data/versioned artifact stores, model registries, or experiment tracking.
 * Experience with annotation systems, dataset inspection tooling, or active-learning workflows.