Principal AI Product Engineer

Houston;
New York;
San Francisco;
Seattle

Nscale is taking on the hyperscalers by building a vertically integrated GenAI cloud platform. We own the data centers, software, and applications that power today's AI stack using sustainable technology solutions. We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As a Nscaler, you'll build trust through openness and transparency, where everyone is inspired to do their best work.

Collaboration is key, and we work together swiftly and respectfully, embracing adaptability and resilience in all we do.

Nscale is looking for a
Principal AI Engineer (Specialised) to lead the inference and post-training pillar of our AI systems engineering organization. You’ll define the multi-year technical roadmap for how models are served, evaluated, and post-trained on Nscale’s GPU cloud, across dedicated and serverless inference and bring-your-own-model deployments. You’ll lead the most consequential architectural programmes in that space and set the engineering standards that 20–50+ engineers build to.

As a Principal engineer, you are one of the deepest technical authorities in the company on AI systems. Your decisions set the cost, latency, and reliability at which Nscale serves tokens and runs post-training workloads, and those numbers have to compete with the world’s leading AI infrastructure providers. The problems span the full stack: kernel efficiency on state-of-the-art GPU systems, fleet-level KV cache and serving architecture, the evals that prove model quality, and RL loops where inference and training share hardware.

You frame the solutions the organization executes against, including the API contracts customers see.

• Dog years.We move quickly and compress a lot of learning into a short time.
• Don’t let perfect be the enemy of good.Ship, measure, iterate.
• Be relentless.Own the problem end to end and see it through.
• One team, one mission.Outcomes over process, and no “not my job”.

• Define and own the multi-year technical roadmap for Nscale’s inference, evals, and post-training platform, and translate it into architecture that multiple teams can execute against
• Lead company-scale architectural initiatives in the pillar, such as next-generation serving (disaggregated prefill/decode, KV cache orchestration across GPU, host, and storage tiers, speculative decoding, multi-tenant scheduling), GPU kernel and model efficiency work (custom kernels, FP8/NVFP4/INT8/4 quantization, sparsity, distillation, MoE serving), evals and benchmarking frameworks, and post-training and RL infrastructure
• Establish engineering standards adopted across all AI teams: API design and compatibility guarantees, benchmarking and evals methodology, training stability norms, and performance testing practices
• Own the framework by which cost, latency, throughput, and model quality trade-offs are made and measured across the pillar
• Identify long-horizon systemic risks early (serving engine and framework bets, accelerator support, capability gaps) and resolve them before they block the organization
• Align AI engineering, research, product, and infrastructure leadership on multi-team technical strategy; frame technical trade-offs in product and commercial terms
• Mentor and develop Staff and Senior AI Engineers, and grow the next generation of inference technical leaders at Nscale
• Represent Nscale’s technical approach externally: open-source leadership in the frameworks we depend on, publications, conference talks, and partnerships with GPU vendors and AI labs

• 10–15 years of engineering experience, with a clear track record…