Principal AI Product Engineer

Nscale•Seattle, WA

4d•$290,000 - $443,333

About The Position

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.

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

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

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