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.
Every serious AI team evaluating infrastructure asks the same question: will this actually run my workload better, faster, or cheaper than what I have today? Most vendors answer with slides. We want to answer with benchmarks on the customer's own workload, and then design the production architecture that delivers on them.
As our Founding Solutions Architect, your mission is to make Prime Intellect the technical winner in every deal. You will own the path from workload discovery through architecture, benchmark, proof of concept, and production design. You will sit with customers' ML engineers to understand what they are actually training and serving — GPU type, cluster requirements, networking, utilization and performance objectives — then design the right architecture on Prime infrastructure, run the POC against their baseline and the competitive alternatives, and turn latency, throughput, utilization, and cost results into a technical business case that holds up under scrutiny.
This is a founding role. There is no playbook yet for how we prove technical wins; you will write it. The benchmark methodologies, reference architectures, and POC frameworks you build will become how Prime Intellect sells to every lab and enterprise that follows.
Technical Discovery & Architecture
Lead deep technical discovery across training, post-training, and inference workloads
Understand each customer's GPU requirements, cluster topology, networking, storage, utilization patterns, and performance objectives
Design customer architectures on Prime infrastructure that meet those objectives and are ready for production
Partner with engineering on migration and deployment requirements so what we design is what gets shipped
Benchmarks, POCs & the Technical Business Case
Own workload benchmarks and proofs of concept against customer baselines and competitive alternatives
Translate latency, throughput, utilization, and cost results into a defensible technical business case for ML engineers and executive buyers alike
Set the standard for benchmark quality: methodology, reproducibility, and honest comparison
Drive time to technical validation down with every deal
Playbooks & Platform Adoption
Develop repeatable benchmark and reference-architecture playbooks that scale beyond you
Support technical evaluation and adoption of Model Factory, bringing customers from compute onto the full training stack
Feed what you learn from customer workloads back into product and research priorities
Strong ML infrastructure, distributed systems, cloud, or GPU-compute background
Working knowledge of modern training and inference stacks, CUDA/GPU environments, and model-serving patterns
Customer-facing experience in solutions architecture, sales engineering, forward-deployed engineering, or a similar role
Ability to communicate fluently with both ML engineers and executive stakeholders — and to know which conversation you are in
A strong bias toward proving claims with real customer workloads and benchmarks rather than slides
High ownership — you see gaps and build the fix before anyone asks
AI-native in how you work: you use LLMs, automation, and programmatic tools to move faster
Bonus:
Hands-on experience running or optimizing large-scale distributed training (FSDP, Megatron, DeepSpeed, or similar) or high-throughput inference (vLLM, SGLang, TensorRT-LLM)
Experience benchmarking across hardware generations (H100, H200, GB200) and interconnects (InfiniBand, RoCE)
Background at a GPU cloud, neocloud, hyperscaler AI team, or frontier lab infrastructure team
Experience building a solutions or forward-deployed function from scratch
Technical win rate · POC-to-commercial conversion · Time to technical validation · Benchmark quality · Deployment readiness · Model Factory technical attach
You will be the technical face of Prime Intellect in every deal that matters, working directly with the ML teams at labs and enterprises deciding where to build — and with our own research and engineering teams on what the platform needs to win. You will see more real-world training and inference workloads in a year than most engineers see in a career, and you will define how a new category of infrastructure proves itself.
Cash compensation range of $250–$320k + meaningful equity
Flexible work (remote or San Francisco), with travel to customer sites as needed
Visa sponsorship and relocation support
Professional development budget
Team off-sites and conferences
A front-row seat to building the infrastructure layer for open AI