Member of Technical Staff - Compute Platform

Prime Intellect; San Francisco (CA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Hybrid work model

Open-source contributions encouraged

Job summary

Prime Intellect is building a platform that unifies AI workloads, compute, environments, and monitoring for frontier-scale models. This hybrid role spans platform software engineering and infrastructure, focusing on Python-based backend services, real-time tooling, and scalable training infrastructure.

You’ll contribute to development of REST APIs, real-time dashboards, and backend features while collaborating across engineering to ensure reliability and security as we open-source and

Qualifications

• Proficiency in Python for backend systems (FastAPI, async) and API services.
• Frontend skills with modern frameworks (TypeScript, React/Next.js).
• Experience with Kubernetes, cloud platforms, and infrastructure automation tools.

Responsibilities

• Develop intuitive web interfaces for AI workload management and monitoring.
• Build REST APIs and backend services; support real-time debugging tools.
• Design distributed training infra, automation pipelines, and resource management features.

Skills

Python backend

TypeScript/React

Rust

Kubernetes

GCP

Tools

Ansible

Terraform

Docker

Job description

Own Your Intelligence

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Core Technical Responsibilities

This hybrid role spans across our AI platform software engineering and infrastructure. You’ll be instrumental in:

Platform Development

• Build intuitive web interfaces for AI workload management and monitoring
• Develop REST APIs and backend services in Python
• Create real-time monitoring and debugging tools
• Implement user-facing features for resource management and job control

Infrastructure Development, Automation & Reliability

• Design and implement distributed training infrastructure in Rust
• Build high-performance networking and coordination components
• Create infrastructure automation pipelines with Ansible
• Manage cloud resources and container orchestration
• Implement scheduling systems for heterogeneous hardware (CPU, GPU, TPU)

Backend & Feature Development

• New Feature Engineering: Collaborate with the engineering team to design and implement backend features.
• API and Service Development: Enhance our platform’s REST APIs and backend services to support new capabilities and improve overall performance.
• System Integration: Ensure seamless integration of new features into our existing infrastructure, maintaining high reliability and security standards.

Development & Infrastructure Skills

• Backend Engineering: Proficiency in Python for developing automation scripts, REST APIs, and backend support tools.
• Container & Cloud Technologies: Hands‑on experience with Kubernetes and cloud platforms (GCP preferred).

Technical Requirements

Platform Skills

• Strong Python backend development (FastAPI, async)
• Modern frontend development (TypeScript, React/Next.js, Tailwind)
• Experience building developer tools and dashboards
• RESTful API design and implementation

Infrastructure Skills

• Systems programming experience with Rust
• Infrastructure automation (Ansible, Terraform)
• Container orchestration (Kubernetes)
• Cloud platform expertise (GCP preferred)
• Observability tools (Prometheus, Grafana)

Nice to Have

• Experience with GPU computing and ML infrastructure
• Knowledge of AI/ML model architecture and training
• High-performance networking implementation
• Open-source infrastructure contributions
• WebSocket/real-time systems experience

Growth Opportunity

You’ll join a team of experienced engineers and researchers working on cutting‑edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open‑source contributions.

We value potential over perfection - if you’re passionate about democratizing AI development and have experience in either platform or infrastructure development (ideally both), we want to talk to you.

Ready to help shape the future of AI? Apply now and join us in our mission to make powerful AI models accessible to everyone.