Open-Source AI Research Platform Engineer

Prime Intellect AI; San Francisco (CA); Remote; USD 150,000 - 300,000; Full time

Benefits offered by this job

Cash compensation

Equity incentives

Flexible work options

Visa sponsorship

Professional development

Team off-sites

Job summary

Prime Intellect AI is building the open frontier AI stack and seeks an Applied Research role that connects research artifacts to open-source systems and product experiences. You will work closely with engineering, shape researcher-facing features, and help external contributors ship high-quality research into the stack.

You will own important open-source RL, environments, and evaluation systems, and collaborate with customers and partners to ensure robust interfaces and reusable artifacts.

Qualifications

• Ownership of open-source ML or developer-tool systems.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation loops.
• Strong experimental judgment: baselines, trace inspection, and failure attribution.
• Strong Python and the ability to build artifacts others can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners updated.

Responsibilities

• Own the path from emerging research artifact to open-source and product experience.
• Collaborate with engineering to shape researcher-facing features in the training platform.
• Define stable interfaces and publish APIs, examples, and reference implementations.
• Work with researchers and external contributors to improve interfaces and research artifacts.

Skills

Open-source ML

Research engineering

Python

Experimental judgement

Job description

Prime Intellect AI is building the open frontier AI stack and seeks an Applied Research role that connects research artifacts to open-source systems and product experiences. You will work closely with engineering, shape researcher-facing features, and help external contributors ship high-quality research into the stack.

You will own important open-source RL, environments, and evaluation systems, and collaborate with customers and partners to ensure robust interfaces and reusable artifacts.