Lead Research Scientist - Post-Training

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 Lead Research Scientist to own and lead 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.

You will be expected to set technical direction in these areas, make key design decisions, and help guide the work of other researchers and engineers while remaining deeply hands‑on.

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.
• Set technical direction and priorities for post-training and reinforcement-learning work.
• 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.
• Provide technical guidance and mentorship to other researchers and engineers working on training and post‑training.
• 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…