TensorWave
ML Performance Engineer
Las Vegas, Nevada · On-site · Posted today
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- Salary
- Not listed
- Job type
- Full-time
- Work mode
- On-site
- Source
- Ashby (employer's hiring system)
Skills mentioned
Kubernetes, MLOps, Python, PyTorch, Linux
About the role
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 accomodations@tensorwave.com.
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.
Job ID ab-tensorwave-e0ca2ee5-4419-4e7f-b7d6-e90996970809 · Original posting ↗