Benchmarking and Performance Specialist

Reposted 3 Days Ago
Be an Early Applicant
2 Locations
Remote
Mid level
Hardware • Information Technology • Software
The Role
Join our data center team to profile performance of open source models using CI/CD, Linux, and dashboard technologies. Build performance dashboards and work with novel hardware platforms.
Summary Generated by Built In

About Positron AI

Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs).


Through lower power usage and drastically lower total cost of ownership (TCO), Positron enables you to run popular open source LLMs to serve multiple users at high token rates and long context lengths. Positron is also designing its own ASIC to expand from inference and fine tuning to also support training and other parallel compute workloads.

About the role

  • You'll be joining our data center team, profiling performance on various open source models across hardware and software builds

What you'll do

  • CI/CD, Linux administration, Ansible configurations, Plotly and Typescript dashboard front ends

Qualifications

  • Experience building performance dashboards
  • Experience with novel hardware platforms
  • BA/BS required, preferred in technical subject like Computer Science, Physics, Applied Math, or Electrical Engineering

Top Skills

Ansible
Ci/Cd
Linux
Plotly
Typescript
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The Company
HQ: Reno, NV
42 Employees
Year Founded: 2023

What We Do

Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs). Through lower power usage and drastically lower total cost of ownership (TCO), Positron enables you to run popular open source LLMs to serve multiple users at high token rates and long context lengths. Positron is also designing its own ASIC to expand from inference and fine tuning to also support training and other parallel compute workloads.

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