Founding Solutions Architect

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.

Your Role

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.

Responsibilities

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

What We're Looking For

• 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

How We Measure Success

Technical win rate · POC-to-commercial conversion · Time to technical validation · Benchmark quality · Deployment readiness · Model Factory technical attach

Why This Role

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.

What We Offer

• 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