Junior AI Researcher

Giotto.ai

. Read and discuss recent AI research and translate promising ideas into experiments .

Posted 10/1/2026full-timeLausanne • SwitzerlandJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in training and evaluating Transformer-based models, with a strong foundation in machine learning and deep learning principles. Proficient in Python and PyTorch, with the ability to translate AI research into practical applications and collaborate effectively across teams.

Highest-signal resume keywords

Master’s Degree In Computer ScienceExperience With PythonFamiliarity With Transformer ArchitecturesExperience With Hugging Face TransformersUnderstanding Of Probability And Statistics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningModel Fine-TuningLinear AlgebraOptimisationResearch Code DevelopmentModel EvaluationSynthetic Data GenerationExperiment DocumentationAnalytical Thinking

Soft Skills

CuriosityClear CommunicationCollaborative Mindset

Tools & Technologies

PyTorchHugging Face DatasetsExperiment-Tracking ToolsCloud InfrastructureGPUs

Industry Keywords

Artificial IntelligenceLarge Language ModelsReinforcement LearningGenerative AIResearch Publications

Tech Stack

Tools & technologies

CloudPythonPyTorch

About the role

Key responsibilities & impact

Read and discuss recent AI research and translate promising ideas into experiments
Train, fine-tune and evaluate Transformer-based models
Contribute to experiments involving reasoning, synthetic data, model adaptation and evaluation
Build benchmarks and analyse model performance and failure cases
Explore improvements to training data, objectives, prompts and inference strategies
Write clean and reproducible research code
Document experiments, results and conclusions
Collaborate with research, engineering and product colleagues
Learn how advanced research is transformed into production-ready AI systems

Requirements

What you’ll need

Completed or nearly completed Master’s degree in computer science, artificial intelligence, machine learning, mathematics, physics or a related quantitative field
Solid understanding of machine learning and deep learning fundamentals
Practical experience with Python and PyTorch through university projects, a thesis, research work or an internship
Familiarity with Transformer architectures and large language models
Understanding of probability, statistics, linear algebra and optimisation
Ability to read research papers and implement ideas from them
Curiosity, analytical thinking and willingness to learn
Clear communication skills and collaborative mindset
Experience with Hugging Face Transformers or Datasets
Master’s thesis related to LLMs, reasoning, multimodal learning, reinforcement learning, interpretability or generative AI
Experience with model fine-tuning, LoRA/PEFT, synthetic data or evaluation pipelines
Research publications, open-source contributions or relevant personal projects
Experience using GPUs, experiment-tracking tools or cloud infrastructure

Benefits

Comp & perks

Hybrid working model
Regular collaboration at the Lausanne office