Senior Staff Engineer, ML Ops

San Mateo, California

Live, first seen by us 65 days ago

Pay: USD 280,000–420,000/year — pay source
Salary 280,000 – 420,000 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

Design and build the Kubernetes-native AI Factory platform for training, simulation and deployment
Build self-service workflows that take engineers from local experiments to distributed training
Design and optimize shared GPU infrastructure across cloud and on-premises environments
Build dataset, experiment, model versioning and deployment capabilities
Package repeatable deployments for cloud, customer, sovereign and air-gapped environments

From the employer’s posting

Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform

Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution

Design and optimize shared GPU infrastructure across cloud and on-premises environments.

Build platform capabilities that enable dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, monitoring, and continuous model improvement.

Develop repeatable deployment and lifecycle management solutions using Infrastructure as Code and modern platform engineering practices.

What you’ll bring

All qualifications

Core experience

Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads.
Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques.
Experience operating GPU-accelerated infrastructure and distributed training systems.
Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems.
Experience with GPU scheduling concepts and large-scale AI workloads.
Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.

Preferred experience

Experience with Ray or other distributed AI orchestration frameworks.
Experience with KAI, Slurm or other GPU scheduling technologies.
Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads.
Experience deploying and optimizing AI models for edge hardware.

Qualification wording

Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads.

Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques.

Experience operating GPU-accelerated infrastructure and distributed training systems.

Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems.

Experience with GPU scheduling concepts and large-scale AI workloads.

Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.

Experience with Ray or other distributed AI orchestration frameworks.

Experience with KAI, Slurm or other GPU scheduling technologies.

Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads.

Experience deploying and optimizing AI models for edge hardware.

Apply at Shieldai

Posting record

Report an error

Status in our records: Live
First seen by us: Aug 31, 202665 days ago
Recorded sightings: 130
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

$280K–$420K against $166K–$240K, the middle half of what similar US postings state.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 65

Similar jobs usually come down between day 6 and day 69.

From 4,301 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.

Go
Python
Grafana
Kubernetes
Terraform
Hugging Face
PyTorch
Transformers

Where the posting names them

- Experience packaging and deploying cloud-native infrastructure using Terraform and Helm. - Strong software engineering skills in Python and Golang and modern cloud-native technologies. - Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities.

- Experience designing infrastructure for classified, sovereign, or air-gapped environments. - Experience with observability technologies such as OpenTelemetry, Prometheus, and Grafana. - Experience contributing to or maintaining open-source infrastructure projects.

Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments. We are building the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems. The AI Factory serves two purposes. Internally, it powers autonomy development across Hivemind and other AI programs. Externally, it becomes the reference architecture deployed into customer environments, spanning commercial cloud, on-premise infrastructure, sovereign deployments, and fully air-gapped systems.

What you'll do: - AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment. - AI Research Enablement: Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.

- Engineers move from idea to distributed training in hours instead of days. - High GPU utilization through efficient scheduling with KAI on Kubernetes. - Researchers use modern AI tooling without unnecessary platform friction.

Required qualifications: - Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads. - Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques.

- Experience operating GPU-accelerated infrastructure and distributed training systems. - Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems. - Experience with GPU scheduling concepts and large-scale AI workloads.

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

- Experience with GPU scheduling concepts and large-scale AI workloads. - Experience packaging and deploying cloud-native infrastructure using Terraform and Helm. - Strong software engineering skills in Python and Golang and modern cloud-native technologies.

- Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads. - Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques. - Experience operating GPU-accelerated infrastructure and distributed training systems.

Upload resume Add LinkedIn

Your next step

Complete your application on jobs.lever.co. The employer’s form will show what is required.

Applying through Lever

No account needed: one form, and Lever reads your resume into the profile recruiters search.

Already applied? Track this application

Has Shieldai posted this job before?

Yes. We’ve followed one other Senior Staff Engineer, ML Ops job there since May 2026. It is still up.

San DiegoStill up after 174 days, first seen May 14
This jobUp 65 days so far, first seen Aug 31

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

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:

Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments. 

We are building the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems.

The AI Factory serves two purposes. Internally, it powers autonomy development across Hivemind and other AI programs. Externally, it becomes the reference architecture deployed into customer environments, spanning commercial cloud, on-premise infrastructure, sovereign deployments, and fully air-gapped systems.

We are looking for a Senior Staff Engineer to help define and build this platform. You will partner closely with ML researchers, platform engineers, and autonomy teams to deliver an exceptional developer experience for training and deploying modern AI models.

Success in this role requires balancing researcher productivity, platform simplicity, operational excellence, and long-term maintainability. You will work hands-on across the stack, helping shape both the platform architecture and its implementation while staying closely aligned with the rapidly evolving AI ecosystem.

What you'll do:

AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment.
AI Research Enablement: Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.
Developer Experience: Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks.
Distributed AI Infrastructure: Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads. Evaluate and integrate orchestration, scheduling, and resource management technologies to maximize scalability and developer productivity.
Compute Platform: Design and optimize shared GPU infrastructure across cloud and on-premises environments. Improve resource utilization, scheduling efficiency, storage, networking, observability, and overall platform reliability.
Data & Model Lifecycle: Build platform capabilities that enable dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, monitoring, and continuous model improvement.
Platform Distribution: Develop repeatable deployment and lifecycle management solutions using Infrastructure as Code and modern platform engineering practices. Support commercial cloud, customer-managed infrastructure, sovereign environments, and fully air-gapped deployments.
Technology Leadership: Evaluate emerging AI infrastructure technologies and establish architectural patterns that balance scalability, performance, maintainability, and developer experience.
Cross-Functional Collaboration: Work closely with AI researchers, autonomy teams, infrastructure engineers, and product teams to ensure the platform evolves alongside customer needs and advances in AI.

Key Outcomes:

Engineers move from idea to distributed training in hours instead of days.
High GPU utilization through efficient scheduling with KAI on Kubernetes.
Researchers use modern AI tooling without unnecessary platform friction.
AI Workspaces become the standard development environment across autonomy programs.
Training, simulation, evaluation, and deployment operate as a unified platform.
The AI Factory Reference Architecture can be deployed consistently across cloud, on-premise, and air-gapped environments.
Platform capabilities are reusable across multiple autonomy programs and customer deployments.

Required qualifications:

Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads.
Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques.
Experience operating GPU-accelerated infrastructure and distributed training systems.
Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems.
Experience with GPU scheduling concepts and large-scale AI workloads.
Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.
Strong software engineering skills in Python and Golang and modern cloud-native technologies.
Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities.

Preferred qualifications:

Experience with Ray or other distributed AI orchestration frameworks.
Experience with KAI, Slurm or other GPU scheduling technologies.
Experience supporting reinforcement learning, simulation-driven training, robotics, or autonomy workloads.
Experience deploying and optimizing AI models for edge hardware.
Experience designing infrastructure for classified, sovereign, or air-gapped environments.
Experience with observability technologies such as OpenTelemetry, Prometheus, and Grafana.
Experience contributing to or maintaining open-source infrastructure projects.

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

280,000 – 420,000 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.

###

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

Source details