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Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Improve Giotto’s models through post-training and reasoning techniques
- Develop and test new ideas for post-training and inference
- Design computational experiments, analyse results, and iterate on findings
- Build reliable research code and experimentation and evaluation tools
- Collaborate with researchers and engineers to integrate successful approaches into models and systems
- Follow relevant research and assess methods for improving model performance and efficiency
Requirements
What you’ll need- Strong programming skills, particularly Python
- Excellent scientific and mathematical thinking
- Strong mathematical and algorithmic thinking; LeetCode Hard and beyond
- Solid grasp of LLM fundamentals is a strong plus
- Experience with PyTorch or similar deep learning frameworks (nice to have)
- Familiarity with LLMs, transformer architectures, and model evaluation (nice to have)
- Experience with supervised fine-tuning, reinforcement learning, preference optimisation, or inference-time reasoning (nice to have)
- Experience with distributed computing, GPU workloads, or efficient inference (nice to have)
- Research background in computer science, mathematics, physics, or another quantitative field (nice to have)
- Evidence of exceptional problem-solving ability through research, technical projects, competitive programming, or mathematics and science competitions (nice to have)
- Prior LLM experience is not required
- No prior experience with every listed tool is required
Benefits
Comp & perks- Hybrid work with remote work support
- Team gathers in the Swiss office for one week per month
- Full-time employment in Switzerland