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.
We are looking for a Senior Software Engineer to build the backend services that make those workflows reliable. You will connect training runs to their data, code and configuration, manage model artifacts and help machine-learning engineers spend less time reconstructing what happened in an experiment.
Build experiment-tracking and model-registry services, integrating tools such as MLflow where appropriate.
Connect exact dataset, code and configuration versions to training runs, checkpoints and model versions.
Develop APIs and adapters that connect training jobs, data services and artifact storage.
Make interrupted or failed workflows recoverable through reliable state handling, retries and useful diagnostics.
Make checkpoints, model artifacts and evaluation results easy to find and retrieve for further training, evaluation or deployment.
Work directly with machine-learning engineers to simplify their workflows and reduce manual record keeping.
Deliver tested services with observability and compatible upgrades across supported cloud and customer-managed environments.
Engineers can trace a model version back to the data, code, configuration and run that produced it.
Checkpoints and evaluation evidence remain available when jobs fail or workflows change.
Developers can compare experiments and retrieve model artifacts through consistent APIs.
Qualification
Experience designing APIsExperience handling storageUnderstanding of training runsExperience with MLflowExperience integrating Kubernetes jobsExperience with reproducibility
Required
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
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.
