Design, build, and optimise scalable AI platform systems spanning (one or more):

Drive inference performance and efficiency, including:

KV cache management, continuous batching, speculative decoding

Quantisation (INT8/4, FP8), sparsity, pruning, and model compression

Build and improve post-training services, including:

Fine-tuning (LoRA, QLoRA, adapters, full fine-tuning)

Alignment (RLHF, DPO, reward modelling)

Agentic RL (tool calling, off-policy training, parallel thinking, decoupled sampling and updating)

Dataset curation and data processing workflows

Develop evaluation and benchmarking systems to measure:

Model quality, safety, and regression

System performance (latency, throughput, cost)

Real-world behaviour and feedback loops

Develop and optimise distributed systems for GPU/accelerator workloads, focusing on scalability, reliability, and efficiency

Conduct performance analysis and bottleneck investigations across multiple components and stacks spanning training, post-training, and inference

Collaborate with research, infrastructure, and product teams to build the right platform components based on customer demand and industry direction

Build developer-facing APIs, SDKs, and tooling that enable other engineers to effectively use Nscale’s AI services

5+ years of experience building production systems in machine learning, distributed systems, or high-performance infrastructure

4+ years of hands-on experience in at least one core area, within large-scale, production AI environments (e.g., AI labs, hyperscalers), such as:

Inference optimisation

Large-scale training / pre-training systems

Post-training (fine-tuning, alignment, distillation)

Evaluation and benchmarking frameworks

Strong hands-on expertise in at least one of the above areas, with working knowledge across others

Proven ability to design, optimise, and operate systems at scale, with a strong understanding of performance trade-offs across latency, throughput, cost, and model quality

Deep understanding of transformer architectures, LLMs, and/or multimodal models, including their behaviour in production systems

Strong proficiency in Python and PyTorch, with a track record of building production-grade ML systems

Experience with distributed compute and training paradigms (e.g., data/model parallelism, sharding, scheduling)

Experience working close to the hardware/software boundary, such as:

GPU/accelerator optimisation (CUDA, ROCm, or similar)

Memory management and system-level performance tuning

Experience building or operating production inference or training systems at scale

Ability to design clean abstractions, APIs, and reusable systems for other engineers

Strong engineering fundamentals, with a track record of writing maintainable, well-tested, production-quality code