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Member of Technical Staff - AI Data - Job Opportunity at Microsoft

Zürich, Switzerland
Full-time
Senior
Posted: June 17, 2025
On-site
CHF 140,000 - CHF 180,000 per year (approximately USD 155,000 - USD 200,000), reflecting Switzerland's premium tech market, Microsoft's competitive positioning, and the specialized nature of large-scale AI data engineering roles

Benefits

Industry-leading healthcare coverage providing comprehensive medical, dental, and vision benefits that exceed standard market offerings
Extensive educational resources including tuition reimbursement, professional development courses, and access to cutting-edge learning platforms
Significant employee discounts on Microsoft products and services, plus partnerships with major retailers and service providers
Robust savings and investment programs including 401(k) matching, stock purchase plans, and financial planning resources
Generous parental leave policies for both maternity and paternity that support work-life balance during major life transitions
Flexible time-off policies that promote employee wellbeing and prevent burnout in high-intensity roles
Corporate giving programs and volunteer time off that allow employees to contribute to meaningful causes
Extensive networking opportunities through internal communities, professional groups, and industry conferences

Key Responsibilities

Design and architect scalable data pipelines capable of ingesting petabytes of multimodal training data across text, audio, images, and video formats, directly impacting the development of next-generation AI models
Build and maintain mission-critical infrastructure systems that serve as the foundation for Microsoft's most advanced AI model training operations, influencing the company's competitive position in the AI market
Drive strategic experimentation initiatives by partnering with pretraining and post-training teams to optimize data recipes, directly contributing to model performance improvements that affect millions of users
Lead cross-functional collaboration efforts with product teams and researchers across Microsoft AI to identify and address critical gaps in current AI model capabilities, shaping the future direction of AI product development
Champion Microsoft's organizational culture and values while working on projects that define the next generation of AI technology

Requirements

Education

Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field OR Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field OR equivalent experience

Experience

Experience in business analytics, data science, software development, data modeling or data engineering work

Required Skills

Experience using data processing technologies for Multimodal dataset scalability, parellel processing, data handling, streaming/batch processing Experience working with distributed computing tools such as; Spark, Kubernetes, TensorFlow, Flink and Pyspark Experience conducted research in Machine Learning or worked as an ML Engineer/ MLOps/ SWE Experience designing and developing data pipelines that ingest enormous amounts of multi-modal training data (text, audio, images, video) AND have the skills to be able to build infrastructure to support this work from ground up Experience working with large scale of data ideal Petabyte scale or above
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Sauge AI Market Intelligence

Industry Trends

The AI industry is experiencing unprecedented growth in multimodal AI systems that can process and understand multiple types of data simultaneously, with companies racing to build comprehensive datasets that span text, images, audio, and video. This represents a fundamental shift from single-modal AI systems to more sophisticated, human-like AI capabilities that can understand context across different media types. Large-scale data infrastructure has become the primary competitive differentiator in AI model development, with organizations investing billions in data collection, processing, and storage capabilities. The ability to handle petabyte-scale datasets efficiently is now considered essential for training state-of-the-art AI models that can compete in the current market. The field is witnessing a consolidation around a few major players who have the resources to build and maintain the massive infrastructure required for frontier AI model development. This creates high-value opportunities for engineers who can work at this scale, but also increases the technical complexity and responsibility associated with these roles.

Role Significance

Likely part of a 15-25 person interdisciplinary team combining data engineers, ML engineers, and research scientists, with this role serving as a key technical contributor in the data engineering domain
This is a senior individual contributor role with significant technical leadership responsibilities, requiring the ability to make architectural decisions that impact Microsoft's core AI infrastructure and model development capabilities

Key Projects

Building and optimizing petabyte-scale data ingestion pipelines for multimodal AI training data Developing infrastructure to support real-time and batch processing of diverse data types including text, audio, images, and video Implementing distributed computing solutions that can scale across thousands of nodes for data processing and storage Creating data quality and validation systems for AI training datasets that directly impact model performance

Success Factors

Deep expertise in distributed computing systems and the ability to design solutions that can scale to handle petabytes of data efficiently, as this directly impacts the team's ability to support cutting-edge AI model training Strong collaboration and communication skills to work effectively across interdisciplinary teams of engineers and researchers, particularly important given the complex nature of AI data pipeline development Proactive problem-solving mindset with the ability to anticipate and address infrastructure challenges before they impact model training operations Attention to detail and data quality, as errors or inefficiencies in data processing can have cascading effects on model performance and training costs Adaptability to rapidly changing priorities and technologies in the fast-moving AI field, where new requirements and approaches emerge frequently

Market Demand

Extremely high demand with limited supply of qualified candidates, as the intersection of petabyte-scale data engineering, multimodal AI systems, and distributed computing expertise represents a rare and highly sought-after skill combination

Important Skills

Critical Skills

Distributed computing expertise using tools like Spark, Kubernetes, and TensorFlow is absolutely essential, as the role requires building systems that can process petabytes of data across distributed infrastructure. Without this foundation, it's impossible to meet the scale requirements of modern AI training data pipelines. Multimodal data processing experience is crucial because the role specifically focuses on handling diverse data types including text, audio, images, and video simultaneously. This requires understanding the unique challenges and optimization strategies for each data type while maintaining system performance. Large-scale data pipeline design and development skills are fundamental to the role's success, as the primary responsibility involves creating robust, scalable systems that can reliably ingest and process enormous volumes of training data without failures that could disrupt model training operations.

Beneficial Skills

Research experience in machine learning provides valuable context for understanding how data processing decisions impact model performance, enabling more informed architectural choices MLOps and model lifecycle management knowledge helps bridge the gap between data engineering and model training teams, facilitating better collaboration and system integration Cloud-native development skills, particularly with Azure services, would accelerate onboarding and enable leveraging Microsoft's full cloud infrastructure capabilities for data processing solutions

Unique Aspects

This role offers the opportunity to work on what Microsoft claims will be "the world's most advanced multimodal dataset," representing a chance to contribute to potentially groundbreaking AI capabilities
The position involves direct collaboration with teams that develop consumer-facing Copilot experiences, providing visibility into how large-scale data engineering work translates into products used by millions of people
Working at petabyte scale with multimodal data presents unique technical challenges that few organizations can offer, providing exceptional learning and career development opportunities
The interdisciplinary team structure combining engineers and scientists creates an environment for continuous learning and exposure to cutting-edge AI research

Career Growth

2-4 years to advance to principal-level individual contributor roles, or 3-5 years to transition into engineering management, depending on performance and organizational needs

Potential Next Roles

Principal Data Engineer or Staff Engineer roles with broader technical leadership responsibilities across multiple AI data projects Engineering Manager positions leading teams focused on AI data infrastructure and large-scale data processing Solutions Architect roles designing enterprise-scale AI data platforms for Microsoft's cloud services Research Engineer positions at the intersection of AI research and data infrastructure development

Company Overview

Microsoft

Microsoft is one of the world's largest technology companies with a market capitalization exceeding $2 trillion, and has established itself as a leader in cloud computing, productivity software, and artificial intelligence. The company's significant investments in AI research and development, including partnerships with OpenAI and development of Copilot products, position it at the forefront of the AI revolution.

Microsoft holds a dominant position in the enterprise software market and is one of the top three cloud service providers globally, competing directly with Amazon and Google in the AI infrastructure space
The Zürich office represents Microsoft's significant investment in European AI talent and research capabilities, taking advantage of Switzerland's strong technical education system and central European location for accessing diverse talent pools
Microsoft has transformed its culture over the past decade to emphasize collaboration, continuous learning, and innovation, with a strong focus on empowering employees to make significant technical contributions while maintaining work-life balance
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