Staff, Platform Engineer AI Compute (6034)

Description

A venture-backed defense-tech company builds intelligent systems to protect service members and civilians. It has sites across the U.S., Europe, the Middle East and Asia-Pacific.

Machine-learning engineers need dependable GPU access to train and evaluate their models. The company's AI Factory offers shared compute and development tools for internal engineers and for customer deployments.

You will make that compute platform simpler to use, more dependable and more efficient. The work is hands-on software and platform engineering: writing code, improving how teams share GPUs, helping developers debug workloads, and using performance data to plan capacity.

The posting also refers to this role as Senior Staff Engineer.

Responsibilities

• Build and maintain the software and configuration that let developers launch GPU jobs on Kubernetes.
• Automate deployment and upgrades of GPU services, including Run:ai, using infrastructure as code with Terraform and Helm.
• Design quotas, scheduling priorities, permissions and isolation so teams can share compute predictably.
• Find the causes of waiting, failing or slow jobs, and fix recurring scheduling, configuration, storage and networking problems.
• Build diagnostics that link queue times, GPU utilization, memory needs and training throughput to what developers experience.
• Benchmark typical workloads and advise when to add GPU capacity, how much to add and which hardware suits the workload.
• Deliver repeatable installations and upgrades across cloud, on-premises and air-gapped environments.
• Developers get the GPUs they need through clear access rules and resource policies.
• Teams can tell capacity shortages apart from configuration faults when training slows or stalls.
• GPU expansion is based on measured demand and workload performance.
• Platform changes raise reliability without disrupting existing training workflows.

Requirements

• Experience building and running production Kubernetes platforms, including GPU or distributed computing workloads.
• Strong software engineering skills in Python, Go or a similar language.
• Experience automating infrastructure provisioning, configuration and upgrades.
• Hands-on troubleshooting across Linux, networking and storage.
• Experience with shared resource scheduling, access controls or workload isolation.
• Ability to work closely with machine-learning engineers, understand their workloads and turn repeated problems into maintained platform features.
• Preferred: experience with Run:ai or comparable GPU scheduling and resource-management systems.
• Preferred: familiarity with distributed training and tools such as Ray or PyTorch.
• Preferred: experience delivering on-premises or air-gapped platforms, or contributing to open-source infrastructure.

Conditions

Temporary employee offer: pay falls within the range listed for the role.

Salary depends on factors including skill set, experience, licenses and certifications, and work location.

Offers are contingent on a cleared background check and possibly a reference check.

Questions about pay should go to your talent acquisition representative.

The company is an equal opportunity and affirmative action employer and welcomes accommodation requests.

Benefits

• Temporary benefits begin after 60 days of employment. Military fellows and part-time employees are not eligible.
• Full-time regular employees receive a separate offer package.