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Salary: USD 250,000 - 320,000 / annual
Prime Intellect is building the open superintelligence stack—infrastructure that frontier AI labs build internally, now available to every ambitious AI team. The platform unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale, from SFT and RL to tool use, agent workflows, and continuously improving production models. The company has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and exceptional operators including Andrej Karpathy, Dwarkesh Patel, and leaders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, and others.
As Founding Solutions Architect, you will own the technical path from workload discovery through architecture, benchmark, proof of concept, and production design. Your mission is to make Prime Intellect the technical winner in every deal. You will sit with customers' ML engineers to understand their actual training and serving requirements—GPU type, cluster topology, networking, utilization and performance objectives—then design the right architecture on Prime infrastructure, run POCs against their baseline and competitive alternatives, and translate latency, throughput, utilization, and cost results into a technical business case.
This is a founding role with no existing playbook. You will write the benchmark methodologies, reference architectures, and POC frameworks that become how Prime Intellect sells to every lab and enterprise that follows.
Key responsibilities include: leading deep technical discovery across training, post-training, and inference workloads; understanding GPU requirements, cluster topology, networking, storage, utilization patterns, and performance objectives; designing customer architectures on Prime infrastructure ready for production; owning workload benchmarks and POCs against baselines and competitors; translating results into defensible technical business cases for ML engineers and executives; setting standards for benchmark quality, methodology, and reproducibility; developing repeatable playbooks and reference architectures that scale; supporting technical evaluation and adoption of Model Factory; and feeding customer learnings back into product and research priorities.
You will be the technical face of Prime Intellect in every major deal, working directly with ML teams at labs and enterprises deciding where to build, and with Prime's own research and engineering teams on platform needs. You will see more real-world training and inference workloads in a year than most engineers see in a career.
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 similar role
- Ability to communicate fluently with both ML engineers and executive stakeholders
- 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
