Specialised AI Engineer

About the role

<p><strong>About Nscale</strong></p> <p>Nscale is taking on the hyperscalers by building a vertically integrated GenAI cloud platform. We own the data centres, 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.</p> <p><strong>About the Role</strong></p> <p>Nscale is looking for <strong>Senior / Staff AI Engineers</strong> to join our core AI team and build the systems that power our GenAI cloud platform.</p> <p>This role sits at the heart of our AI services platform, designing and optimising distributed systems that power large-scale training, post-training, evaluation, and low-latency, high-throughput inference under strict performance and efficiency constraints.</p> <p>You may specialise deeply in areas such as <strong>inference optimisation, large-scale training, post-training (fine-tuning, alignment), or evaluation systems</strong>, or operate across multiple parts of the stack. In all cases, you’ll work on <strong>hard systems problems at scale</strong>, where performance, efficiency, and developer experience are critical.</p> <p>This is a hands-on role for engineers who want to push the boundaries of how AI systems are built, optimised, and consumed by other AI engineers.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Design, build, and optimise <strong>scalable AI platform systems</strong> spanning (one or more): <ul> <li>Drive <strong>inference performance and efficiency</strong>, including: <ul> <li>KV cache management, continuous batching, speculative decoding</li> <li>Quantisation (INT8/4, FP8), sparsity, pruning, and model compression</li> </ul> </li> <li>Build and improve <strong>post-training services</strong>, including: <ul> <li>Fine-tuning (LoRA, QLoRA, adapters, full fine-tuning)</li> <li>Alignment (RLHF, DPO, reward modelling)</li> <li>Agentic RL (tool calling, off-policy training, parallel thinking, decoupled sampling and updating)</li> <li>Dataset curation and data processing workflows</li> </ul> </li> <li>Develop <strong>evaluation and benchmarking systems</strong> to measure: <ul> <li>Model quality, safety, and regression</li> <li>System performance (latency, throughput, cost)</li> <li>Real-world behaviour and feedback loops</li> </ul> </li> </ul> </li> <li>Develop and optimise <strong>distributed systems for GPU/accelerator workloads</strong>, focusing on scalability, reliability, and efficiency</li> <li>Conduct performance analysis and bottleneck investigations across multiple components and stacks spanning training, post-training, and inference</li> <li>Collaborate with research, infrastructure, and product teams to build the right platform components based on customer demand and industry direction</li> <li>Build <strong>developer-facing APIs, SDKs, and tooling</strong> that enable other engineers to effectively use Nscale’s AI services</li> </ul> <p><strong>Requirements</strong></p> <ul> <li><strong>5+ years of experience</strong> building production systems in machine learning, distributed systems, or high-performance infrastructure</li> <li><strong>4+ years of hands-on experience in at least one core area</strong>, within large-scale, production AI environments (e.g., AI labs, hyperscalers), such as: <ul> <li>Inference optimisation</li> <li>Large-scale training / pre-training systems</li> <li>Post-training (fine-tuning, alignment, distillation)</li> <li>Evaluation and benchmarking frameworks</li> </ul> </li> <li>Strong hands-on expertise in <strong>at least one</strong> of the above areas, with working knowledge across others</li> <li>Proven ab

Skills