T

TensorWave

Scalable AI cloud computing for PyTorch

ML Engineer - Developer Ecosystem

Applied Machine LearningPosted on Nov 13
Full-timeLas Vegas, NV, USA

About the Job

Requirements

  • A Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
  • At least 3 years of experience building machine learning systems in Python.
  • Strong experience with PyTorch and the Hugging Face ecosystem.
  • Hands-on experience with large language model 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.
  • Be based in the San Francisco Bay Area and be available for regular in-person events and occasional travel.

Responsibilities

  • Build and maintain 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 the platform.
  • Deliver technical content for workshops and hackathons, and support participants during events.
  • Partner with the marketing team on technical content by contributing topics, outlines, and technical review.
  • Share insights from events and the developer community to inform marketing and machine learning priorities.
  • Collaborate with machine learning engineering teams and contribute issue reports and fixes to upstream projects.

Desired 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.

About the Company

TensorWave

Scalable AI cloud computing for PyTorch

TensorWave provides cloud-based compute power for large-scale AI workloads, with a focus on PyTorch. It runs on AMD MI300X hardware with a scalable fabric that can pool vast amounts of VRAM, and customers access this power through a subscription or pay-as-you-go model. The service allocates the requested compute and storage behind a managed fabric, charging based on usage to offer flexibility and cost-efficiency. TensorWave aims to be a leading AI cloud provider by offering large VRAM pools and strong price‑to‑performance, helping enterprises and research institutions train, fine-tune, and deploy large models without owning hardware.

Company size
51-200
Company stage
Series B
Total funding
$493M
Headquarters
Las Vegas, NV
Founded
2023

Recently Posted Jobs

Explore more opportunities at TensorWave

See more jobs