Software Engineer – ML Platform

Location: Austin, Texas, United States; onsite. Candidates must be authorized to work in the U.S. Remote work is not available, and relocation and visa sponsorship are not offered.

Company

hirify.global develops autonomous-driving technology and the infrastructure required to train and operate machine-learning systems at scale.

What you will do

• Build and scale the ML compute platform on Kubernetes using Argo Workflows for training, evaluation, and data-processing orchestration.
• Design platform capabilities including a Ray-based SDK for distributed execution and multi-tenant resource governance for GPU, CPU, memory, and I/O.
• Optimize training throughput and platform efficiency through improved data access, caching, and reduced storage, network, and resource bottlenecks.
• Work with ML engineers to debug complex workloads, perform root-cause analysis, and convert recurring issues into platform-level fixes.
• Evaluate, integrate, and extend open-source tooling across the Kubernetes ecosystem.

Requirements

• Strong proficiency in Python or Go.
• Experience designing and building scalable, maintainable systems and services.
• Experience operating production services end to end, including APIs, reliability practices, and observability.
• Deep knowledge of Kubernetes scheduling, resource management, controllers, and pod lifecycle behavior.
• Solid Linux and systems-debugging skills covering performance, networking, and storage/I/O.
• Ability to troubleshoot production issues across logs, metrics, and traces and drive them to resolution.

Nice to have

• C++ experience.
• Experience with Argo Workflows, Ray, MLflow, or comparable distributed ML tooling.
• Experience building or operating large-scale ML training systems, including GPU scheduling, distributed training, and training-data pipelines.
• Experience optimizing resource usage and performance in distributed environments.

Culture & Benefits

• Work directly with ML teams across the company to improve experimentation and model training at scale.
• Build production-grade tooling for the full machine-learning model lifecycle.
• Reasonable accommodations are available for qualified applicants and employees with disabilities.
• Equal-opportunity employment practices apply.