# Senior Solutions Architect (US) at LumAI

LumAI is hiring a Senior Solutions Architect (US) in San Francisco, United States. Level rates it Works on AI ●●●○; you can [apply on LumAI](https://jobsbylevel.com/go/e452980e-c2e0-4852-9b13-7c40aa957cd6?utm_medium=ai-agent).

AI Level 3, AI centrality 71 out of 100. San Francisco, Bay Area.

## Details

- Company: [LumAI](https://jobsbylevel.com/companies/lumai)
- AI level: AI Level 3 (score 71 out of 100)
- Location: San Francisco, Bay Area
- Posted: June 17, 2026
- Apply: https://jobsbylevel.com/go/e452980e-c2e0-4852-9b13-7c40aa957cd6?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-solutions-architect-us-at-lumai-8bcc75

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-solutions-architect-us-at-lumai-8bcc75

## Description

The Role We are looking for a technically deep and commercially astute Solutions Architect to own the technical heart of our customer engagements as we bring the world's first optical compute inference platform to market. You will be the person who turns a customer's interest into a credible technical opportunity. You will lead deep technical discovery with the most demanding customers in the industry – hyperscalers, neoclouds, frontier AI labs, and national laboratories – and translate what they need into a Lumai-based solution. You will work hands-on with our benchmark team to model and prove performance against real customer workloads, and you will author the technical proposals and responses that allow a customer to make the argument for Lumai inside their own organisation. This is an early-stage role where you will help write the playbook. You will be as comfortable in a benchmarking discussion with a customer's ML and performance engineers as you are presenting a solution to their technical leadership, and you will act as a vital channel back into our own product and engineering teams, ensuring the voice of the customer shapes what we build next. In the near term, much of the work will be focused on pre-hardware performance modelling and simulation. What You Will Do Partner with Lumai’s VP Business Development, owning the technical relationship with each customer Conduct deep technical discovery with customer ML, inference and infrastructure engineering teams – their models, serving stacks and performance targets Translate customer requirements into a Lumai-based solution, mapping needs against both near-term and roadmap solutions With business development, define the shape of an engagement – benchmark, proof of concept, reference architecture, or statement of work Work hands-on with Lumai's benchmark team to scope, run, and interpret benchmarks against GPU and other baselines, and own the presentation of results Be the voice of the customer in refining Lumai’s customer-facing simulation framework, and support customers in running their own workloads on it Build performance and TCO models – throughput, latency, cost and energy per token – showing how Lumai compares with incumbent solutions on customer workloads Produce the technical case for Lumai – proposals, solution documentation, and responses to RFIs, RFPs, and tenders Design and deliver demos and technical workshops for customer technical teams Work with engineering to define how Lumai integrates with customer software stacks – serving frameworks, runtimes and orchestration Feed customer requirements, evaluation feedback, and roadmap reactions back into product and engineering, informing the Lumai roadmap What We Are Looking For Must-have At least five years’ experience in a solutions architecture, technical pre-sales, or senior field engineering role within AI infrastructure, high-performance compute, semiconductors, or a comparable deep-tech hardware domain Strong working knowledge of LLM inference at scale – model architectures, serving frameworks such as vLLM, TensorRT-LLM or SGLang, batching and parallelism, and the trade-offs between throughput, latency and cost Hands-on experience benchmarking and analysing the performance of AI workloads, including performance modelling, with the credibility to defend results to customer ML and infrastructure engineers Demonstrated ability to lead technical discovery with sophisticated customers and translate requirements into a solution Comfortable operating in a fast-moving, sometimes ambiguous, pre-product environment Desirable Direct experience selling into, or working within, hyperscalers, neoclouds, frontier AI labs, or national laboratories Awareness of the AI accelerator competitive landscape Background or genuine curiosity in novel compute – photonics, optical computing, or other post-silicon architectures Experience with accelerator software stacks – compilers, kernels and runtimes – or bringing a new accelerator

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