Software Engineer (SE / Sr SE), Data & ML Platform
Santa Clara, CA
- Pay
USD 135,000–200,000/year — pay source
Salary 135,000 – 200,000 USD per year Employment type
Read the full posting- Work setup
- Unconfirmed
- Employment
Full-time — employment source
Employment type Full-time
Read the full posting
What you’ll work on
Full postingBuild safe, repeatable GitOps-based delivery for platform services and user applications using tools such as Argo CD, Helm, and Kustomize
Build and improve reusable distributed batch and workflow platforms for Spark data processing and GPU-based replay and simulation
Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts
From the employer’s posting
Operate and evolve our production Kubernetes clusters end to end: bare-metal provisioning automation, highly available control planes, node lifecycle, GPU container runtime, networking, and storage Build safe, repeatable GitOps-based delivery for platform services and user applications using tools such as Argo CD, Helm, and Kustomize Develop shared multi-tenant platform capabilities for scheduling, resource isolation, storage, networking, access control, secrets, and observability while improving CPU/GPU utilization and cost efficiency
Develop shared multi-tenant platform capabilities for scheduling, resource isolation, storage, networking, access control, secrets, and observability while improving CPU/GPU utilization and cost efficiency Build and improve reusable distributed batch and workflow platforms for Spark data processing and GPU-based replay and simulation Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts
Build and improve reusable distributed batch and workflow platforms for Spark data processing and GPU-based replay and simulation Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts Required Skills:
What you’ll bring
All qualificationsCore experience
- Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience
- Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management
Preferred experience
- Experience with Ray or Kubeflow
- Experience with lakehouse technologies such as Delta Lake or Apache Iceberg
- Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator
Qualification wording
Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience
Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management
Experience with Ray or Kubeflow
Experience with lakehouse technologies such as Delta Lake or Apache Iceberg
Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator
Education & alternatives
Required Skills: - BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience - Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience
Tools in this posting
- Delta
- Kubernetes
- Spark
- Iceberg
Source — Tool mentions in context
- Experience with Ray or Kubeflow - Experience with lakehouse technologies such as Delta Lake or Apache Iceberg - Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator
All key offline workloads — large-scale data processing, simulation, auto-labeling, scenario mining, and model training — run on the compute platform this role owns. In this role, you will improve the reliability and efficiency of our Kubernetes infrastructure, make workload onboarding simpler and more self-service, and build reusable batch and workflow capabilities for petabyte-scale processing. We are looking for strong Kubernetes and platform-engineering fundamentals, depth in at least one adjacent area—distributed data processing, ML/GPU infrastructure, or multi-tenant compute systems—and the curiosity and ownership to grow across the others. We are open to candidates at either the Software Engineer or Senior Software Engineer level. Level will be determined by experience, technical depth, scope of ownership, and demonstrated impact. You do not need experience with every technology in our stack; we value strong fundamentals, ownership, and the ability to learn.
Responsibilities: - Operate and evolve our production Kubernetes clusters end to end: bare-metal provisioning automation, highly available control planes, node lifecycle, GPU container runtime, networking, and storage - Build safe, repeatable GitOps-based delivery for platform services and user applications using tools such as Argo CD, Helm, and Kustomize
- BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience - Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience - Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management
- Experience with lakehouse technologies such as Delta Lake or Apache Iceberg - Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator Salary
Job description
All key offline workloads — large-scale data processing, simulation, auto-labeling, scenario mining, and model training — run on the compute platform this role owns. In this role, you will improve the reliability and efficiency of our Kubernetes infrastructure, make workload onboarding simpler and more self-service, and build reusable batch and workflow capabilities for petabyte-scale processing. We are looking for strong Kubernetes and platform-engineering fundamentals, depth in at least one adjacent area—distributed data processing, ML/GPU infrastructure, or multi-tenant compute systems—and the curiosity and ownership to grow across the others.
We are open to candidates at either the Software Engineer or Senior Software Engineer level. Level will be determined by experience, technical depth, scope of ownership, and demonstrated impact. You do not need experience with every technology in our stack; we value strong fundamentals, ownership, and the ability to learn.
Responsibilities:
-
Operate and evolve our production Kubernetes clusters end to end: bare-metal provisioning automation, highly available control planes, node lifecycle, GPU container runtime, networking, and storage
-
Build safe, repeatable GitOps-based delivery for platform services and user applications using tools such as Argo CD, Helm, and Kustomize
-
Develop shared multi-tenant platform capabilities for scheduling, resource isolation, storage, networking, access control, secrets, and observability while improving CPU/GPU utilization and cost efficiency
-
Build and improve reusable distributed batch and workflow platforms for Spark data processing and GPU-based replay and simulation
-
Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts
Required Skills:
-
BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience
-
Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience
-
Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management
-
Self-driven with a strong sense of ownership: a quick learner who is eager to take responsibility and drive projects forward end to end
Preferred Skills:
-
Experience with Ray or Kubeflow
-
Experience with lakehouse technologies such as Delta Lake or Apache Iceberg
-
Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator
Salary
135,000 – 200,000 USD per year
Employment type
Full-time
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 135,000 – 200,000 USD per year Employment type
- Location & working pattern
Santa Clara, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Aug 5, 2026
- Recorded sightings
- 21
- Last seen by us
- Oct 8, 2026
- Employer says posted
- Jul 31, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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