Microsoft AI предлагает влиятельную работу над предобучением передовых моделей, понятную инженерную зону ответственности и зарплату заметно выше рыночного ориентира платформы. Широкие диапазоны для уровней IC4-IC5 всё же добавляют неопределённости.
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TL;DR
Pretraining Data (AI): Creating and evaluating large-scale datasets and scalable data pipelines for frontier AI foundation models with an accent on dataset quality, data analysis, and responsible AI. Focus on running data ablation experiments, building dataset auditing tools, and improving data ingestion, processing, filtering, and annotation workflows.
Location: Zurich, Switzerland
Base pay: CHF 146,200–245,900 per year for Software Engineering IC4; CHF 183,800–275,800 per year for Software Engineering IC5.
Company
Microsoft AI develops frontier AI models and products, with this role supporting foundation model pretraining and deployment.
What you will do
Design and curate high-quality datasets for training and evaluation, and run data ablation experiments to assess their impact.
Develop and maintain scalable pipelines for data ingestion, processing, filtering, and annotation.
Analyze real-world datasets for quality, diversity, and relevance, identifying areas for improvement.
Build lightweight tools and workflows for dataset auditing, visualization, and versioning.
Collaborate with Safety, Ethics, and Governance teams on quality, privacy, and responsible AI standards.
Requirements
Bachelor’s degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or a related technical discipline and 4+ years of technical engineering experience, including coding in Python and common data libraries such as Pandas and NumPy, or equivalent experience.
Nice to have
Bachelor’s degree in a relevant technical discipline and 10+ years of technical engineering experience with Python and common data libraries, or a relevant master’s degree and 8+ years of experience, or equivalent experience.
2+ years of experience in data analysis or data engineering with large-scale unstructured or semi-structured datasets.
Proficiency in statistics and exploratory data analysis methods.
Familiarity with Spark, Ray, or Apache Beam.
Ability to communicate technical findings clearly to research and product teams.
Culture & Benefits
Cross-disciplinary collaboration with scientists, engineers, and annotators.
Work focused on research and engineering that improves shipped AI models and products.
Academic publishing is not a priority for this role.
Certain roles may be eligible for benefits and other compensation.
Microsoft is an equal opportunity employer and provides reasonable accommodations during the application process.
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