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# Machine Learning Engineer

- **Company:** G P U Nuclear, Inc
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Build Automation, Computer Engineering, Software Debugging, Linux, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Kubernetes, Slurm, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-tensorwave-7810616

## About the Role

* Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
 * Expertise supporting production ML systems using SLURM and Kubernetes
 * Strong understanding of GPU-accelerated workloads and distributed systems concepts
 * Solid Linux fundamentals and experience debugging infrastructure-level issues
 * Ability to build automation and tooling - Python, Go, etc., * Experience working across schedulers, orchestration platforms, or cluster managers
 * Familiarity with large-scale GPU environments or HPC-style systems
 * Experience improving infrastructure reliability, utilization, or performance at scale

## Description

We're looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you'll be responsible for building and operating the core systems that power large-scale ML training and inference across TensorWave's GPU platform, working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact.

What You'll Do

 * Design, operate, and improve ML infrastructure systems supporting distributed training and inference workloads
 * Build reliable, repeatable workload execution and orchestration patterns across shared GPU environments
 * Troubleshoot performance, reliability, and scalability issues across the ML stack
 * Partner with ML, systems, and platform teams to improve developer experience and operational efficiency


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