Staff Software Engineer ML Platform (6244)

San Diego, California

Actively hiring, first seen by us 1 day ago

Pay: USD 200,000–300,000/year — pay source
Salary 200,000 – 300,000 USD per year #LI-DM2
Read the full posting
Work setting: On-site — work setting source
Work setup in the employer’s posting: onsite
From the employer’s posting
Employment: Full-time — employment source
Employment type Full Time Employee
Employment type in the employer’s posting: Full Time Employee
From the employer’s posting

What you’ll work on

Full posting

Build experiment-tracking and model-registry services, integrating tools like MLflow where fit
Connect dataset, code and config versions to training runs, checkpoints and model versions
Develop APIs and adapters linking training jobs, data services and artifact storage
Make interrupted or failed workflows recoverable with state handling, retries and diagnostics
Make checkpoints, artifacts and evaluation results easy to find and retrieve

From the employer’s posting

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.

What you’ll bring

All qualifications

Core experience

Experience building production backend services for machine-learning platforms or data-intensive workflows.
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

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.

Qualification wording

Experience building production backend services for machine-learning platforms or data-intensive workflows.

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.

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.

Apply at Shieldai

Posting record

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Actively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 2, 20261 day ago
Recorded sightings: 3
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

Higher than most similar jobs

$200K–$300K against $118K–$193K, the middle half of what similar US postings state.

From 3,721 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 1

Early. Similar jobs usually come down between day 7 and day 55.

From 9,990 similar postings that came down. Coming down is not always a hire.

Shieldai is hiring

11 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring is picking up
Its jobs tend to stay up a long time

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Kubernetes
MLflow

Where the posting names them

- 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 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.

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. Salary

What you'll do: - 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.

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.

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About the role

We file this job under Data Scientist, by the work the posting describes.

Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.

Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.

Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.

US government figures for data scientists

Median yearly pay, May 2025: $120,230
Expected change, 2025 to 2035: 34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗

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 www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Job 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.

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.

What you'll do:

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.

Required qualifications:

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.

Why Join Us

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.

Salary

200,000 – 300,000 USD per year

#LI-DM2

#LF

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.

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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

Employment type

Full Time Employee

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