Founding Solutions Architect

Prime Intellect • San Francisco, CA

10d • $250,000 - $320,000 • Hybrid

About The Position

Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. They are developing open-source models for long-horizon tasks and the platform to build them. The company has raised $150M from prominent investors and individuals in the AI and infrastructure space. They are seeking individuals to contribute to the intersection of frontier research, infrastructure, and go-to-market strategy in a nascent category.

As the Founding Solutions Architect, your primary responsibility is to establish Prime Intellect as the technical leader in every customer engagement. This involves owning the entire process from understanding customer workloads to designing architectures, conducting benchmarks, executing proofs of concept (POCs), and finalizing production designs. You will collaborate closely with customers' ML engineers to grasp their specific training and serving needs, including GPU types, cluster configurations, networking, utilization, and performance goals. You will then architect solutions on Prime infrastructure, run POCs against customer baselines and competitors, and translate performance metrics (latency, throughput, utilization, cost) into a robust technical business case. This is a foundational role where you will create the playbook for how Prime Intellect demonstrates technical wins, developing benchmark methodologies, reference architectures, and POC frameworks that will be used across all future sales engagements.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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
• 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
• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed