Specialised AI Engineer

Senior Manager, Internal Communications - Greenhouse•Technology

DeutschlandFull-timeOn sitelast month

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📄Stellenbeschreibung

About Nscale

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.

About the Role

Nscale is looking for Senior / Staff AI Engineers to join our core AI team and build the systems that power our GenAI cloud platform.

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.

You may specialise deeply in areas such as inference optimisation, large-scale training, post-training (fine-tuning, alignment), or evaluation systems , or operate across multiple parts of the stack.

In all cases, you’ll work on hard systems problems at scale , where performance, efficiency, and developer experience are critical.

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.

Responsibilities

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

Requirements

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

Preferred

Experience developing large-scale and high-load production systems.

Experience working in containerised, distributed environments (e.g., Kubernetes, large-scale clusters)

Experience contributing to or working with widely used/open-source AI frameworks or systems is strongly preferred

Hands-on experience with advanced inference optimisation techniques, such as KVCache, MoE, adaptive batching, or gradient checkpointing.

Experience developing APIs using OpenAPI 3.0+ specifications.

Knowledge of efficient training and inference evaluation strategies, with demonstrated success in improving model efficiency.

At Nscale, we are committed to fostering an inclusive, diverse, and equitable workplace.

We believe that a variety of perspectives enriches our work environment, and we encourage applications from candidates of all backgrounds, experiences, and abilities.

We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.

If there’s anything we can do to accommodate your specific situation, please let us know.

The responsibilities outlined in this job description are not exhaustive and are intended to provide a general overview of the position.

The employee may be required to perform additional duties, tasks, and responsibilities as assigned by management, consistent with the skills and qualifications required for the role

For information on how Nscale handles candidate personal data, please see our Employee & Candidate Privacy Notice: Here.

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