> Markdown version of [/jobs/ext/4581801-staff-software-engineer-ml-platform-6244](https://www.wearedevelopers.com/jobs/ext/4581801-staff-software-engineer-ml-platform-6244). 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). --- # Staff Software Engineer ML Platform (6244) - **Company:** Shield AI - **Location:** San Mateo, CA, United States - **Experience:** Expert - **Salary:** $200,000 - $300,000 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Distributed Systems, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, Backend, Kubernetes, Free and Open-Source Software, Machine Learning Operations, Software Version Control - **Published:** November 5, 2026 - **Apply:** https://www.dice.com/job-detail/789d1d93-6c27-4f50-a2dd-ee595638c725 ## About the Role * Experience building production backend services for machine-learning platforms or data-intensive workflows. * Strong software engineering skills in Python or a comparable backend language. * Experience designing APIs, metadata models and persistent state for services used by other engineers. * Experience handling storage, partial failures, retries and consistency in distributed systems. * Understanding of training runs, checkpoints and model versioning. * Ability to work directly with machine-learning users and deliver maintainable software that solves their workflow problems. Preferred qualifications: * Experience with MLflow, Weights & Biases, model registries or comparable experiment-management tools. * Experience integrating Kubernetes jobs, object storage or machine-learning workflow systems. * Experience with reproducibility, data provenance or evaluation records. * Experience supporting open-source software or on-premises and air-gapped deployments. ## Description Model development produces experiments, checkpoints, evaluation results and many versions of the same model. Keeping those records connected lets engineers reproduce results, compare approaches and move a trained model into use.