ML Performance Engineer

Posted Yesterday
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Las Vegas, NV, USA
In-Office
Mid level
Artificial Intelligence • Cloud • Software
The Role
Validate GPU cluster deployments by running large-scale training and inference workloads, diagnose performance and reliability issues, benchmark ML models, and build repeatable validation tooling. The role uses Kubernetes, Slurm, distributed training frameworks, and AMD Instinct GPUs while collaborating with infrastructure, platform, and MLOps teams to improve customer workloads and communicate performance findings.
Summary Generated by Built In

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, 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

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.

What You’ll Do

  • Validate new cluster deployments by running large-scale training and inference workloads before customer handoff

  • Identify, reproduce, and diagnose performance and reliability issues, working with infrastructure and platform teams to resolve them

  • Use our managed Kubernetes and Slurm platforms the way our customers do, and provide feedback that improves them

  • Run model benchmarks across training and inference, both for internal and external use

  • Build tooling and repeatable workflows for validation and benchmarking

  • Stay current with distributed training and inference frameworks and their performance on AMD Instinct GPUs

  • Work with larger MLOps team to help support customer clusters and workloads

Who You Are

Required Qualifications

  • 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

  • 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

Equal Employment Opportunity

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.

Reasonable Accommodations

TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact [email protected].

Employment Eligibility

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

Background Checks

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

Data Privacy Notice

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.

Skills Required

  • Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
  • 3+ years of experience running machine learning workloads on GPU clusters
  • Hands-on distributed training experience with 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
Am I A Good Fit?
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The Company
HQ: Las Vegas, Nevada
56 Employees

What We Do

TensorWave is a cutting-edge cloud platform designed specifically for AI workloads. Offering AMD MI300X accelerators and a best-in-class inference engine, TensorWave is a top-choice for training, fine-tuning, and inference. Visit tensorwave.com to learn more. Send us a message to try it for free.

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