ML Performance Engineer

United States·Las Vegasfull-timemid

Other

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Quick Summary

Overview

About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers,

Technical Tools

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Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure.

About the Role

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We're looking for an ML Performance Engineer to join our MLOps team. You will validate new clusters by running large-scale training and inference workloads, help improve our managed Kubernetes and Slurm platforms through hands-on use, and produce performance benchmarks for customers and the broader market.

Responsibilities

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Validate new cluster deployments by running large-scale training and inference workloads before customer handoff
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Identify, reproduce, and diagnose performance and reliability issues, working with infrastructure and platform teams to resolve them
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Use our managed Kubernetes and Slurm platforms the way our customers do, and provide feedback that improves them
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Run model benchmarks across training and inference, both for internal and external use
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Build tooling and repeatable workflows for validation and benchmarking
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Stay current with distributed training and inference frameworks and their performance on AMD Instinct GPUs
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Work with larger MLOps team to help support customer clusters and workloads

Requirements

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Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
3+ years of experience running ML workloads on GPU clusters
Hands-on experience with distributed training using frameworks such as PyTorch FSDP, Megatron-LM, DeepSpeed, or torchtitan
Working knowledge of Slurm and Kubernetes from a user and workload perspective
Experience profiling and optimizing GPU workloads, including collective communication performance
Strong Python skills and solid Linux fundamentals
Clear written communication of performance findings to engineering and non-technical audiences

What We Offer

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✓Stock Options

✓100% paid Medical, Dental, and Vision insurance for Employees

✓Company Health Savings Account Contributions

✓100% paid Short Term and Long Term Disability Insurance for Employees

✓Life and Voluntary Supplemental Insurance Options

✓Other Insurance Options, such as Pet & Legal Insurance

✓Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support

✓Flexible Spending Account

✓401(k)

✓Employee Assistance Program

✓Flexible PTO

✓Paid Holidays

✓Parental Leave

✓Other In-Office Perks

TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law.

TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact accomodations@tensorwave.com.

All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law.

Where permitted by law, employment may be contingent upon the successful completion of a job-related background check.

By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.