Senior Machine Learning Researcher
Role details
Job location
Tech stack
Job description
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Researcher in Switzerland.This role offers a unique opportunity to influence the foundation of AI model performance by focusing on the quality and structure of training data. You will lead efforts to curate, assess, and optimize large-scale, unstructured datasets that power state-of-the-art AI systems. Working closely with research and engineering teams, you will apply statistical, computational, and ML-driven techniques to improve dataset diversity, representativeness, and overall impact. The position requires a highly analytical and independent thinker who can design frameworks to evaluate and de-risk datasets while contributing to the development of automated data preprocessing and validation tools. This is a highly collaborative role in a fast-moving, high-trust environment that encourages ownership, experimentation, and measurable impact on real-world AI outcomes.Accountabilities:
- Lead the evaluation, curation, and optimization of large-scale unstructured datasets for AI model training.
- Design and implement statistical and machine learning methods to assess data quality, diversity, and informativeness.
- Collaborate with model training teams to identify data bottlenecks and optimize dataset performance.
- Provide leadership on data quality strategy and establish best practices for dataset assessment.
- Evaluate external datasets for scalability, relevance, and integration, creating data scorecards as needed.
- Contribute to R&D of tools that automate data preprocessing, validation, and enhancement processes.
- Communicate findings and improvements to research, engineering, and cross-functional teams.
Requirements
Requirements:PhD or equivalent Master's degree with 4+ years of industry experience in machine learning, computer science, statistics, mathematics, engineering, or a related quantitative field.Strong understanding of AI training pipelines, including preprocessing, evaluation, and optimization of datasets.Experience handling large, unstructured datasets, particularly in text-based domains.Background in statistical analysis, bias detection, and data validation methodologies.Ability to identify high-impact problems and independently develop solutions.Excellent collaboration and communication skills, with experience working across technical teams.Bonus: experience with synthetic data generation, dataset augmentation, or development of evaluation frameworks; publications or open-source contributions in data-centric AI.Benefits:Competitive compensation and performance-based incentives.Fully remote, flexible working environment.Opportunity to work on high-impact AI projects with access to