---
title: 'Member of Technical Staff - Inference at Prime Intellect'
canonical: 'https://feeny.ai/job/member-of-technical-staff-inference-prime-intellect-7syapkjxntgq'
type: 'job'
last_seen: '2026-09-07'
---

# Member of Technical Staff - Inference at Prime Intellect

- **Company:** Prime Intellect
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/primeintellect/abfa70f7-a6f1-44d2-a6c1-560e1c8477d4

## 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.

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:

1. Building the infrastructure to serve LLMs efficiently at scale.
2. Optimization and integration of inference systems into our RL training stack.

## CORE TECHNICAL RESPONSIBILITIES

LLM Serving

- Multi‑tenant LLM Serving: Build a multi-tenant LLM serving platform that operates across our cloud GPU fleets.
- GPU‑Aware Scheduling: Design placement and scheduling algorithms for heterogeneous accelerators.
- Resilience & Failover: Implement multi‑region/zone failover and traffic shifting for resilience and cost control.
- Autoscaling & Routing: Build autoscaling, routing, and load balancing to meet throughput/latency SLOs.
- Model Distribution: Optimize model distribution and cold-start times across clusters.

Inference Optimization & Performance

- Framework Development: Integrate and contribute to LLM inference frameworks such as vLLM, SGLang, TensorRT‑LLM.
- Parallelism and Configuration Tuning: Optimize configurations for tensor/pipeline/expert parallelism, prefix caching, memory management and other axes for maximum performance.
- End‑to‑End Performance: Profile kernels, memory bandwidth and transport; apply techniques such as quantization and speculative decoding.
- Perf Suites: Develop reproducible performance suites (latency, throughput, context length, batch size, precision).
- RL Integration: Embed and optimize distributed inference within our RL stack.

Platform & Tooling

- CI/CD: Establish CI/CD with artifact promotion, performance gates, and reproducible builds.
- Observability: Build metrics, logs, tracing; structured incident response and SLO management.
- Docs & Collaboration: Document architectures, playbooks, and API contracts; mentor and collaborate cross‑functionally.

## TECHNICAL REQUIREMENTS

Required Experience

- Building ML Systems at Scale: 3+ years building and running large‑scale ML/LLM services with clear latency/availability SLOs.
- Inference Backends: Hands‑on with at least one of vLLM, SGLang, TensorRT‑LLM.
- Distributed Serving Infra: Familiarity with distributed and disaggregated serving infrastructure such as NVIDIA Dynamo.
- Inference Internals: Deep understanding of prefill vs. decode, KV‑cache behavior, batching, sampling, speculative decoding, parallelism strategies.
- Full‑Stack Debugging: Comfortable debugging CUDA/NCCL, drivers/kernels, containers, service mesh/networking, and storage, owning incidents end‑to‑end.

Infrastructure Skills

- Python: Systems tooling and backend services.
- PyTorch: LLM Inference engine development and integration, deployment readiness.
- Cloud & Automation: AWS/GCP service experience, cloud deployment patterns.
- Kubernetes: Running infrastructure at scale with containers on Kubernetes.
- GPU & Networking: Architecture, CUDA runtime, NCCL, InfiniBand; GPU‑aware bin‑packing and scheduling across heterogeneous fleets.

## Nice to Have

- Kernel‑Level Optimization: Familiarity with CUDA/Triton kernel development; Nsight Systems/Compute profiling.
- Systems Performance Languages: Rust, C++.
- Data & Observability: Kafka/PubSub, Redis, gRPC/Protobuf; Prometheus/Grafana, OpenTelemetry; reliability patterns.
- Infra & Config Automation: Terraform/Ansible, infrastructure-as-code, reproducible environments
- Open Source: Contributions to serving, inference, or RL infrastructure projects.

## WHAT WE OFFER

- Cash Compensation Range of $150-300k with significant equity incentives
- Flexible work arrangement (remote or San Francisco office)
- Full visa sponsorship and relocation support
- Professional development budget
- Regular team off-sites and conference attendance
- Opportunity to shape decentralized AI and RL at Prime Intellect

## 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, 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.

## About Prime Intellect

## Company Overview
- **One-liner**: Prime Intellect provides an open, full-stack platform for companies to train, deploy, and continuously improve their own AI models through large-scale distributed reinforcement learning.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Vincent Weisser (CEO), Johannes Hagemann (CTO)

## Core Business
- **Primary industry**: AI infrastructure, open-source AI research, and agentic training platforms
- **Target customers**: B2B — enterprises and AI labs that want to train custom models without building their own infrastructure; also open-source community contributors
- **Mission**: Democratize frontier AI training by making it accessible to every company and collectively owning the resulting open innovations.

## Products & Services
- **Compute**: Access to GPU clusters (H100, H200, B200+) from 50+ providers, plus on-demand single GPU instances and reserved clusters with InfiniBand networking and SLURM/K8s orchestration.
- **Hosted Training (Lab)**: Managed large-scale reinforcement learning (RL) training without infrastructure overhead, including a 2,500+ environment hub and custom eval benchmarks.
- **Inference**: Serverless or dedicated inference for custom models, with native LoRA support and 1-click deployment.
- **Open-Source Libraries**: `prime-rl` (async RL framework), `verifiers` (environments & evals), and environment hub for collaborative development.
- **INTELLECT-3**: A 100B+ parameter Mixture-of-Experts model trained on their RL stack, achieving state-of-the-art performance for its size.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $70.44M across multiple rounds (CBInsights, 2026); earlier reports indicated $20.5M in seed rounds, followed by a Series B round of $49.94M led by Founders Fund and other investors.
- **Notable Investors/Partners**: Founders Fund, CoinFund, Distributed Global, Radical Ventures, Andrej Karpathy, Clem Delangue (Hugging Face), Dylan Patel (SemiAnalysis), Tri Dao (Together AI).
- **Growth Signals**: Headcount grew 128.6% YoY to 36 employees; 24 active job postings (monthly job growth +26.3%); launched INTELLECT-3 and SYNTHETIC-2 datasets; customers include Ramp and Zap; operates in 9 countries.

## Competitive Advantages
- **Full-stack integrated platform** – compute, post-training, evals, and inference in one place, reducing the fragmentation of building and maintaining AI workflows.
- **Large-scale distributed RL expertise** – proven ability to train 100B+ models across clusters (global training via their RL stack).
- **Open-source ethos** – models, datasets, and libraries are released openly, attracting a community of researchers and developers.
- **Founding team & research edge** – strong ties to frontier AI research, with contributions like prime-rl and self-improving agent loops.

## Strategic Focus
- **Self-improving agents** – building infrastructure that closes the loop from deployment back to training, enabling models to compound performance over time.
- **Scaling RL at extreme sizes** – recent research (RL at 1T Scale) shows commitment to massive-scale reinforcement learning.
- **Enterprise adoption** – adding dedicated solutions engineers and managed workflows for corporate customers.

## Why Work Here
- **Culture**: Seeks “the most ambitious developers” and emphasizes cutting-edge research and engineering. The team includes former engineers from Together AI, Aleph Alpha, and Anyscale.
- **Remote/Hybrid**: Remote-friendly with HQ in San Francisco.
- **Growth trajectory**: Rapidly scaling company (headcount up 128% YoY) with multiple open roles across engineering, research, and go-to-market.
- **Tech stack**: Python, PyTorch, CUDA, Kubernetes, SLURM, Grafana, and a broad modern AI toolchain – opportunity to work on infrastructure-level challenges.

## Sources
1. [primeintellect.ai](https://www.primeintellect.ai/)
2. [linkedin.com/company/primeintellect-ai](https://www.linkedin.com/company/primeintellect-ai)
3. [jobs.ashbyhq.com/PrimeIntellect](https://jobs.ashbyhq.com/primeintellect)
4. [cbinsights.com/company/prime-intellect](https://www.cbinsights.com/company/prime-intellect)
5. [docs.primeintellect.ai/introduction](https://docs.primeintellect.ai/introduction)

## Other roles at Prime Intellect

- [Head of Talent](https://feeny.ai/job/head-of-talent-prime-intellect-san-francisco-naf4r2rwx21x) — San Francisco, CA
- [Member of Technical Staff - Training Platform](https://feeny.ai/job/member-of-technical-staff-training-platform-prime-intellect-san-francisco-4btkkz3tw3ps) — San Francisco, CA
- [Member of Technical Staff - Sandbox Platform](https://feeny.ai/job/member-of-technical-staff-sandbox-platform-prime-intellect-san-francisco-qsr7247a01ar) — San Francisco, CA
- [Member of Technical Staff - GPU Infrastructure](https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-prime-intellect-san-francisco-yxe0m7p9mjqy) — San Francisco, CA
- [Member of Technical Staff - Full Stack Software Engineer](https://feeny.ai/job/member-of-technical-staff-full-stack-software-engineer-prime-intellect-san-enhkkcn3cfge) — San Francisco, CA
- [Member of Technical Staff - Compute Platform](https://feeny.ai/job/member-of-technical-staff-compute-platform-prime-intellect-san-francisco-00b0zmttc2p0) — San Francisco, CA
- [Research Engineer - RL Infrastructure](https://feeny.ai/job/research-engineer-rl-infrastructure-prime-intellect-san-francisco-s38mrqzq4m59) — San Francisco, CA
- [Research Engineer - Reinforcement Learning](https://feeny.ai/job/research-engineer-reinforcement-learning-prime-intellect-san-francisco-q3tyazz9enkn) — San Francisco, CA
- [Research Engineer - Distributed Training](https://feeny.ai/job/research-engineer-distributed-training-prime-intellect-san-francisco-6anh0v1a8nqx) — San Francisco, CA
- [Open Application for Unconventional Talent](https://feeny.ai/job/open-application-for-unconventional-talent-prime-intellect-san-francisco-g7rn6nfnxcg1) — San Francisco, CA