Microsoft ML Engineer II 

Estonia, Tallinn 

820997361

We enable data scientists and developers to quickly and easily build, train, deploy, manage, and consume machine learning models.

Required Qualifications:

• Bachelor's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python, KQL
  • OR equivalent experience.
• Depth in Data Science, Generative AI and Software Engineering.
• Proficiency in Agile development practices and Continuous Integration/Continuous Deployment (CI/CD).
• Background in machine learning, deep learning, and natural language processing.
• Experience with:
  • distributed systems design and implementation.
  • transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion).
  • cloud platforms (e.g., Azure, AWS) and distributed computing.

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Experience in a DevOps culture to maintain live services and\or application frameworks.
• Dedicated analytical skills with systematic and structured approach to software design.
• Experience building LLM and Machine Learning solutions.
• Experience with debugging and resolving complex technical issues, particularly when the areas are not well-understood.
• Communication, collaboration, and problem-solving skills.
• Good understanding of statistics, linear algebra, and probability theory.
• Excellent problem-solving skills and the ability to work independently and collaboratively
• Good understanding of statistics, linear algebra, and probability theory.
• Excellent problem-solving skills and the ability to work independently and collaboratively
• A growth mindset and a willingness to learn new things and take on challenges.

As a SW Engineer in our team, you will:

• Collaborate with cross-functional teams, including researchers, software engineers, and product managers.
• You will be expected to meet with stakeholders/PM to get the requirements, document the design and review within the team, implement the design, create unit tests on your changes, manage the flighting of the new feature, and implement additional monitoring and metrics as needed for the feature.
• Design and implement accurate and actionable internal monitoring and tooling to help maintain business Service Level Agreement (SLA) and system health.
• Participate in On Call rotations to support live site and drive engineered solutions to improve customer experiences.
• Design, develop and maintain large scale distributed software services and solutions in a DevOps culture.
• Develop “best-in-class” engineering for our services by ensuring that the services and the components are well-defined and modularized, secure, reliable, diagnosable, actively monitored and reusable.
• Improve test coverage for services, organize and implement integration tests, and resolve problem areas.
• System design through well-defined interfaces across multiple components, code reviews, leveraging data/telemetry to make decisions.
• Focus on customer/partner needs through a data driven approach.
• Deploy trained models in production environments.
• Monitor model performance, troubleshoot issues, and iterate on improvements.
• Work with large-scale datasets, preprocess them, and create appropriate data representations.
• Optimize model performance, scalability, and efficiency.
• Conduct experiments to evaluate model performance, robustness, and generalization.
• Explore novel techniques and approaches to enhance model capabilities.
• Stay up to date with the latest advancements in LLM, NLP, deep learning, and AI research.
• Select relevant features and ensure data quality for training and evaluation.
• Communicate technical findings and insights effectively.