Remote-First Research Scientist: Geo-Distributed Inference

Pluralis-Research; San Francisco (CA); Remote; USD 180,000 - 280,000; Full time

Benefits offered by this job

Equity-heavy package

Remote-first culture

Visa sponsorship & relocation

Open problems

Job summary

Pluralis Research is building Protocol Learning to train and serve large models on consumer hardware via the internet. The role focuses on developing a fast and reliable inference pipeline that can operate end-to-end across distributed nodes, while enabling production-grade serving for RL training and model deployment.

You will design and implement novel methods for fast inference on consumer GPUs, contribute to research publications or presentations, and operate in a remote-first environment

Qualifications

• Shipped serving engines or built a large-scale inference system.
• Publications (papers/blog posts) in distributed inference or LLM serving systems are a strong signal.
• Experience with systems in low-bandwidth, high-latency settings.
• Alignment with Protocol Learning mission is desirable.

Responsibilities

• Own the inference stack end-to-end: pipeline-parallel execution, placement, routing, transport, serving engine, and failure handling.
• Invent algorithms to accelerate inference on consumer hardware over the public internet and validate them in production.
• Serve training and users: keep rollout pipeline fast for RL training now and enable serving for trained models.

Skills

Serving systems

Research ability

Low-bandwidth networks

Mission alignment

Job description

Pluralis Research is building Protocol Learning to train and serve large models on consumer hardware via the internet. The role focuses on developing a fast and reliable inference pipeline that can operate end-to-end across distributed nodes, while enabling production-grade serving for RL training and model deployment.

You will design and implement novel methods for fast inference on consumer GPUs, contribute to research publications or presentations, and operate in a remote-first environment