Job description

About Nscale 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. About the Role 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. How We Work 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”. Responsibilities 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 Requirements 10–15 years of engineering experience, with a clear track record of pillar-level impact on production AI systems 4+ years of hands-on work with LLMs in inference, GPU performance, evals, or post-training and RL, in production or research Demonstrated ability to define multi-year technical strategy for complex, multi-team AI systems organizations World-class depth in production LLM inference, GPU performance, evals, and/or post-training and RL infrastructure, with strong working knowledge across the rest Demonstrated ownership of the architecture of a large-scale production inference or training platform Proven ability to create architectural frameworks and engineering standards adopted across large engineering organizations Deep understanding of the hardware/software boundary for AI accelerators: CUDA or ROCm, memory bandwidth and interconnect constraints, and distributed compute paradigms Strong history of growing technical leaders (Staff and above) and multiplying technical capability across teams External recognition in the AI systems community through research, open source, or industry contribution Preferred Prior experience at a top-tier AI lab or major hyperscaler AI infrastructure team Maintainer or core contributor to a foundational inference, kernel, or RL framework (vLLM, SGLang, TensorRT-LLM, LMCache, FlashInfer, Triton, verl, OpenRLHF, TRL, DeepSpeed, Megatron-LM, etc.) Hands-on depth in RL for LLMs (DPO/GRPO-style methods, reward modelling, multi-turn and tool-use RL) and the interaction between inference and training infrastructure Experience defining developer API platforms adopted at scale by external developers Deep experience with control plane / data plane architecture and cell-based deployment patterns in large-scale inference infrastructure Published work in AI systems: MLSys, NeurIPS Systems Track, OSDI, EuroSys, SC, or equivalent Experience with hardware-software co-design: custom accelerator kernels (CUDA, Triton), compiler-level optimization, AI hardware roadmap engagement Experience defining pricing, SLO, and capacity models for a commercial inference product The range below reflects the base salary for the position. Actual compensation may vary based on job-related factors such as skill set, experience, education, and location. In addition to base salary, this role may be eligible for bonus, equity, and/or commission programs. Nscale may offer a competitive benefits package including medical, dental, vision, flexible paid time off, parental leave, and retirement plan participation. Salary Range $290,000 — $520,000 USD For information on how Nscale handles candidate personal data, please see our Employee & Candidate Privacy Notice: Here. Nscale does not accept unsolicited candidate submissions from recruitment agencies.

Requirements

Must-haves

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
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 of pillar-level impact on production AI systems
4+ years of hands-on work with LLMs in inference, GPU performance, evals, or post-training and RL, in production or research
Demonstrated ability to define multi-year technical strategy for complex, multi-team AI systems organizations
World-class depth in production LLM inference, GPU performance, evals, and/or post-training and RL infrastructure, with strong working knowledge across the rest
Demonstrated ownership of the architecture of a large-scale production inference or training platform

Nice-to-haves

Prior experience at a top-tier AI lab or major hyperscaler AI infrastructure team
Maintainer or core contributor to a foundational inference, kernel, or RL framework (vLLM, SGLang, TensorRT-LLM, LMCache, FlashInfer, Triton, verl, OpenRLHF, TRL, DeepSpeed, Megatron-LM, etc.)
Hands-on depth in RL for LLMs (DPO/GRPO-style methods, reward modelling, multi-turn and tool-use RL) and the interaction between inference and training infrastructure
Experience defining developer API platforms adopted at scale by external developers
Deep experience with control plane / data plane architecture and cell-based deployment patterns in large-scale inference infrastructure
Published work in AI systems: MLSys, NeurIPS Systems Track, OSDI, EuroSys, SC, or equivalent
Experience with hardware-software co-design: custom accelerator kernels (CUDA, Triton), compiler-level optimization, AI hardware roadmap engagement
Experience defining pricing, SLO, and capacity models for a commercial inference product

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aiapillmrest

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Benefits

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Dental

Vision

Paid Time Off

Parental Leave