Senior Research Engineer​/Research Scientist - Post-Training, Reinforcement Learning & Training Systems

Giotto.ai is a Switzerland-based AI company building intelligence systems for Switzerland and Europe. Our mission is to enable governments and enterprises to retain control over the AI systems they use without compromising access to advanced reasoning capabilities. Giotto combines portable, configurable models with an AI operating system, integrating open and proprietary weights, datasets, tools, and deployment components.

We are looking for a Senior Research Engineer or Research Scientist to own the training and optimisation side of our complete post-training stack.

Starting from pretrained checkpoints, you will design, implement, scale, and operate the methods required to produce capable, reliable, and controllable production models.

Your scope will include supervised fine-tuning, preference optimisation, reinforcement learning, reward and verifier integration, policy distillation or consolidation, and distributed training.

This is not a single-GPU fine-tuning or adapter-only role. You should be comfortable operating training workloads where memory, communication, rollout generation, hardware topology, and fault recovery must be designed together.

• Own the end-to-end post-training pipeline from pretrained checkpoint to production candidate.
• Design and execute full-parameter and parameter-efficient SFT.
• Implement preference optimisation, RLHF, RLAIF, reinforcement learning with verifiable rewards, and related methods.
• Develop training strategies for reasoning, coding, tool use, multilingual behaviour, and long-horizon agent tasks.
• Integrate reward models, verifiers, critics, graders, and process- or outcome-based rewards.
• Build scalable rollout-generation systems for iterative and on-policy training.
• Design multi-stage curricula combining SFT, reinforcement learning, rejection sampling, distillation, and policy consolidation.
• Scale training across multiple machines and accelerators using appropriate combinations of data, tensor, pipeline, sequence, context, or expert parallelism.
• Select sharding, precision, checkpointing, optimiser, batch-size, sequence-length, and activation-recomputation strategies.
• Estimate memory, communication, throughput, rollout capacity, and compute requirements before launching major runs.
• Profile and improve accelerator utilisation, communication efficiency, data loading, and end-to-end training time.
• Diagnose numerical instability, communication failures, out-of-memory errors, stragglers, checkpoint issues, and convergence regressions.
• Investigate reward hacking, entropy collapse, KL drift, stale rollouts, mode collapse, grader exploitation, and benchmark overfitting.
• Build reliable checkpointing, recovery, monitoring, and reproducibility procedures.
• Collaborate closely with data, evaluation, infrastructure, and inference teams.
• Contribute clean, tested code, technical reports, and operational runbooks.

• Ownership of large-scale language-model training or post-training runs across multiple machines and accelerators.
• Experience with workloads for which straightforward single-node training or pure data parallelism was insufficient.
• Deep proficiency with Python, PyTorch, autograd, mixed precision, optimisation, and distributed execution.
• Practical experience with PyTorch Distributed, FSDP, Deep Speed, Megatron-Core, or an equivalent framework.
• Ability to select parallelism and sharding strategies based on model, sequence, memory, and network constraints.
• Strong understanding of SFT, preference optimisation, reinforcement learning, reward modelling, KL regularisation, sampling, and training stability.
• Experience operating high-throughput inference or rollout systems as part of a training loop.
• Ability to debug across model code, distributed communication, numerical optimisation, data, and infrastructure.
• Strong experimental design and the ability to distinguish algorithmic improvements from evaluation or systems artefacts.
• Experience building reliable, observable, and reproducible research software.
• Personal ownership of consequential decisions affecting a substantial training programme.

• Python and PyTorch.
• PyTorch Distributed and FSDP.
• Deep Speed, Megatron-Core, or comparable frameworks.
• Hugging Face Transformers.
• CUDA and NCCL.
• vLLM, SGLang, or similar rollout engines.
• Ray, Slurm, Kubernetes, or comparable orchestration systems.
• MLflow or Weights & Biases.
• Docker, GCP, Git Lab CI, profiling, monitoring, and pytest.
• Experience with CUDA or Triton, long-context training, sparse models, asynchronous RL, stateful agent environments, distillation, or deployment-aware post-training would be especially valuable.

• Enjoy working at the intersection of model research and distributed systems.
• Can move from paper reproduction to reliable scaled implementation.
• Are comfortable taking responsibility for expensive and operationally demanding experiments.
• Approach failures methodically across…