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

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.
About the role
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.

You will:

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.

We are looking for demonstrated experience in most of the following areas:

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 DeepSpeed 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.

A PhD is not required. We value exceptional technical work strong judgement and demonstrated ownership.

Relevant stack

Python and PyTorch.
PyTorch Distributed and FSDP.
DeepSpeed 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 GitLab 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.

You may be a strong fit if you:

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 algorithms data numerical stability and infrastructure.
Care about held-out capability and reliability not only training loss or reward.
Want meaningful ownership of a complete model programme.

Location and work style
We offer full-time employment in Switzerland.

Remote work is supported.
The team gathers approximately one week per month in a Swiss office.
Exceptional candidates elsewhere in Europe may be considered..

Required Experience:

Senior IC