Member Of Technical Staff
Posted 8 Jul 2026
Advertised as “Member of Technical Staff - Inference”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
- Visa sponsorship
- Offered
No salary listed for this role.
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.
Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.
Role Impact
This is a hybrid position spanning cloud LLM serving, LLM inference optimization and RL systems. You will be working on advancing our ability to evaluate and serve models trained with our RL Lab at scale. The two key areas are:
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Building the infrastructure to serve LLMs efficiently at scale.
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Optimization and integration of inference systems into our RL training stack.
Core Technical Responsibilities
LLM Serving
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Multi‑tenant LLM Serving: Build a multi-tenant LLM serving platform that operates across our cloud GPU fleets.
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GPU‑Aware Scheduling: Design placement and scheduling algorithms for heterogeneous accelerators.
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Resilience & Failover: Implement multi‑region/zone failover and traffic shifting for resilience and cost control.
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Autoscaling & Routing: Build autoscaling, routing, and load balancing to meet throughput/latency SLOs.