Staff AI Product Engineer

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 Staff AI Engineer (Specialized) to set technical direction for the inference and reinforcement learning systems at the core of our AI services platform — and for the APIs through which other engineers consume them.

This role owns the architecture of how models are served on Nscale’s GPU cloud, how RL and post-training workloads run on it, and how both are exposed to customers and internal teams as clean, reliable, high-performance interfaces. You’ll work across 2–4 teams spanning serving, post-training, and platform, defining how these systems are built and establishing the standards that create engineering leverage across the organization.

As a Staff engineer, you are the technical authority for this area of the AI stack. Your decisions determine the latency, throughput, and cost profile of every token Nscale serves, and the correctness and efficiency of every RL run on our platform. You resolve ambiguous architectural questions where the answer space is genuinely open — disaggregated versus co-located serving, on-policy versus off-policy RL infrastructure, where the API boundary should sit — and your solutions become the standards others build on.

Responsibilities

Inference

• Set technical direction for Nscale’s inference serving architecture: request routing, scheduling, continuous batching, KV cache management, prefix caching, and speculative decoding
• Drive the strategy for model efficiency in production — quantization (FP8, INT8/4), sparsity, pruning, distillation, and MoE serving — and the trade-offs between cost, latency, throughput, and model quality
• Lead the resolution of systemic performance and reliability challenges across the serving stack, from kernel-level bottlenecks to fleet-level capacity and multi-tenant isolation

Reinforcement learning & post-training

• Own the architecture of Nscale’s RL and post-training systems: RLHF, DPO/GRPO-style methods, reward modelling, and agentic RL with tool calling, off-policy training, and decoupled sampling and policy updates
• Define how inference and training share infrastructure in RL loops — rollout generation, sample buffering, weight synchronization, and the serving engine’s role inside the training system
• Establish standards for fine-tuning services (LoRA, QLoRA, adapters, full fine-tuning) and the data curation and processing workflows that feed them

Developer APIs & platform

• Set the design standards for Nscale’s developer-facing APIs, SDKs, and tooling — OpenAI-compatible and native interfaces, OpenAPI 3.x specifications, versioning, and rate limiting — so that inference and RL capabilities are consumable by engineers who never see the underlying systems
• Create reusable frameworks and tooling that multiply the effectiveness of other AI engineers across Nscale

Leadership & direction

• Coach and grow more junior engineers across teams; raise technical capability and engineering quality broadly
• Collaborate with research, product, and infrastructure leadership to align inference and RL platform strategy with customer demand and business direction
• Evaluate emerging serving engines, RL frameworks, and accelerator technologies; make decisive build/adopt/contribute recommendations
• Represent the inference and RL domain in cross-organizational architecture reviews and strategic planning

Requirements

• 8–12 years of engineering experience, with significant depth in production AI systems at scale (AI labs, hyperscalers, or leading ML infrastructure companies)
• Demonstrated ability to set technical direction for an AI systems domain at scale
• Deep expertise in production LLM inference: serving architectures, KV cache and memory management, batching and scheduling strategies, speculative decoding, and low-precision inference
• Strong hands-on expertise in RL for LLMs — RLHF, DPO and related preference-optimization methods, reward modelling, or agentic/multi-turn RL — including the systems that make them run efficiently on GPU clusters
• Proven ability to design developer-facing APIs and SDKs that are clean, versioned, and adopted by other engineers and external customers
• Strong cross-team influence: track record of creating standards and practices adopted by multiple teams
• Experience designing AI infrastructure with clear control plane / data plane separation and cell-based architecture patterns for scale-out and blast-radius isolation
• Experience with large-scale GPU/accelerator workloads: CUDA or ROCm, memory bandwidth optimization, and distributed compute paradigms (data/model/tensor parallelism, sharding, scheduling)
• Strong proficiency in Python and PyTorch, with a track record of building maintainable, well-tested, production-grade ML systems
• Deep understanding of transformer and LLM architectures and their behavior under production load
• Ability to design architecture that is both technically excellent and practically adoptable across a diverse engineering organization

Preferred

• Contributions to widely-used open-source inference or RL frameworks (vLLM, SGLang, TensorRT-LLM, verl, OpenRLHF, TRL, DeepSpeed, etc.)
• Experience at an AI lab (OpenAI, DeepMind, Anthropic, Meta AI, etc.), hyperscaler AI team, or leading ML infrastructure company
• Published research or technical writing on inference systems or RL infrastructure (MLSys, NeurIPS, ICLR systems tracks, blog posts)
• Experience with hardware-software co-design for AI accelerators (custom CUDA kernels, Triton, etc.)
• Experience operating inference platforms in containerized, distributed environments (Kubernetes, large-scale clusters)
• Experience building evaluation and benchmarking systems for model quality, safety, and system performance