ML Engineer, Developer Ecosystem

TensorWave
Las Vegas, NV
Remote

Who this role is best for

Practitioners with ML development experience and event delivery skills will find this remote role with a focus on developer ecosystems and GPU optimization.

Best fit for

  • Candidates with experience in PyTorch and LLM inference frameworks who enjoy creating educational content for developers
    — “Strong experience with PyTorch and the Hugging Face ecosystem”
  • Individuals who thrive in cross-functional collaboration and can translate technical insights into actionable content
    — “Partner with the marketing team on technical content, contributing topics, outlines, and technical review”
  • Professionals who are comfortable with GPU environments and have a track record of working with AMD Instinct or NVIDIA CUDA platforms
    — “Experience with AMD Instinct GPUs and the ROCm ecosystem, or equivalent experience with NVIDIA CUDA”

Things to consider

  • Geographic requirement for in-person events in the San Francisco Bay Area despite the remote work arrangement
    — “Based in the San Francisco Bay Area, with availability for regular in-person events and occasional travel”
  • The role demands active engagement with the developer community and event-based support, which may require flexible scheduling
    — “Support participants during events”

How to stand out

  • Emphasize your experience in creating tutorials or technical documentation for developer audiences
    — “Experience writing technical documentation, tutorials, or blog posts for developer audiences”
  • Highlight your hands-on work with LLM inference frameworks and how it aligns with the role’s needs
    — “Hands-on experience with LLM inference frameworks such as vLLM or SGLang”
  • Showcase your ability to deliver technical workshops or training sessions to demonstrate event engagement skills
    — “Experience delivering workshops, talks, or technical training”
  • Demonstrate your contribution to open source projects to align with the company’s collaborative culture
    — “Open source contributor”
  • Include your background with GPU environments and model training to show relevant technical depth
    — “Proficiency with Linux, containers, and GPU environments”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Build and maintain tutorials and reference solutions
  • Evaluate new models and frameworks
  • Deliver technical content for workshops
  • Partner with marketing on technical content
  • Share insights from events and developer community
Typical background
Bachelor of Science in Computer Science or related field3+ years of experience building ML systemsStrong experience with PyTorch and Hugging Face ecosystem

Skills & requirements

Required

PythonPyTorchHugging Face EcosystemLLM Inference FrameworksLinuxContainersGPU EnvironmentsCommunicationTechnical Content Creation

Preferred

AMD Instinct GpusRocm EcosystemModel Training And Fine-tuningWorkshops And TalksTechnical DocumentationOpen Source Contribution

Stack & domain

PythonPyTorchHugging Face EcosystemLLM Inference FrameworksVllmSglangLinuxContainersGPU EnvironmentsAMD Instinct GpusRocm EcosystemNvidia CUDAModel TrainingFine-tuningWorkshopsTalksTechnical TrainingTechnical DocumentationTutorialsBlog PostsOpen Source ContributionCommunicationMLDeveloper EcosystemAICloud PlatformInfrastructureInnovationDeveloper EventsTechnical ContentDeveloper CommunityMarketingML PrioritiesML EngineeringUpstream ProjectsTechnical Review

About the role

Original posting from TensorWave via Ashby

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 Engineer, Developer Ecosystem to serve as the engineering counterpart to our Developer Ecosystem team. Working within the ML team and reporting to the VP of AI, you will build the tutorials, reference solutions, and workshop content that help developers succeed on TensorWave, and bring hands-on ML expertise to our developer events and technical content.

What You’ll Do

  • Build and maintain a library of tutorials and reference solutions for AMD Instinct GPUs, covering inference, serving, and fine-tuning
  • Evaluate new models and frameworks as they are released and develop working examples on our platform
  • Deliver technical content for workshops and hackathons, and support participants during events
  • Partner with the marketing team on technical content, contributing topics, outlines, and technical review
  • Share insights from events and the developer community to inform marketing and ML priorities
  • Collaborate with ML engineering teams and contribute issue reports and fixes to upstream projects

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 building ML systems in Python
  • Strong experience with PyTorch and the Hugging Face ecosystem
  • Hands-on experience with LLM inference frameworks such as vLLM or SGLang
  • Proficiency with Linux, containers, and GPU environments
  • Strong written and verbal communication skills, including presenting to technical audiences
  • Based in the San Francisco Bay Area, with availability for regular in-person events and occasional travel

Preferred Qualifications

  • Experience with AMD Instinct GPUs and the ROCm ecosystem, or equivalent experience with NVIDIA CUDA
  • Experience with model training and fine-tuning
  • Experience delivering workshops, talks, or technical training
  • Experience writing technical documentation, tutorials, or blog posts for developer audiences
  • Open source contributor

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.

Source: TensorWave careers (Ashby)

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