Staff Software Engineer ML Platform (6244)

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.

• 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.

• 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.

Key Outcomes:

• 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.

• Teams can reproduce results without manually reconstructing experiment history., Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit Follow Shield AI on LinkedIn, X, Instagram, and YouTube., The AI Factory is the foundation for how Shield AI develops and deploys autonomy.

As a Senior Staff Engineer, you will help build the reference architecture used internally by our engineering teams and delivered to customers building their own AI capabilities. Your work will directly impact how quickly researchers can iterate, how efficiently compute resources are utilized, and how AI systems move from experimentation to operational deployment.

This is an opportunity to work at the intersection of modern AI, distributed systems, Kubernetes, and defense, building a platform that enables the next generation of autonomy systems while leveraging the best of the open-source AI ecosystem.

$200,000 - $300,000 a year