Senior Machine Learning Engineer
Las Vegas, Nevada · Remote · Las Vegas, Nevada, United States
- Pay
- Salary not listed in the saved posting
- Work setup
On-site — work setup source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering › Machine Learning — team source
Department: Engineering › Machine Learning
From the employer’s posting
What you’ll work on
Full postingWe’re looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth.
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
Partner with ML, systems, and platform teams to improve developer experience and operational efficiency
From the employer’s posting
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
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
Troubleshoot performance, reliability, and scalability issues across the ML stack Partner with ML, systems, and platform teams to improve developer experience and operational efficiency Who You Are
What you’ll bring
All qualificationsCore experience
- Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
- Ability to build automation and tooling - Python, Go, etc.
- Expertise supporting production ML systems using SLURM and Kubernetes
- Strong understanding of GPU-accelerated workloads and distributed systems concepts
Preferred experience
- 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
Qualification wording
Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
Ability to build automation and tooling - Python, Go, etc.
Expertise supporting production ML systems using SLURM and Kubernetes
Strong understanding of GPU-accelerated workloads and distributed systems concepts
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
Tools in this posting
- Go
- Python
- Kubernetes
Source — Tool mentions in context
- Solid Linux fundamentals and experience debugging infrastructure-level issues - Ability to build automation and tooling - Python, Go, etc. Preferred Qualifications
- 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
Benefits in the posting
Full benefits wording- 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
From the employer’s posting.
About Tensorwave
Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale.
In the employer’s words · Read in context
Job description
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 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
Who You Are
Required Qualifications
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.
Preferred Qualifications
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
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.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Las Vegas, Nevada, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 93
- Last seen by us
- Oct 13, 2026
- Employer says posted
- Nov 17, 2025
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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