Software Engineer II - AI/ML at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Mar, 26

Salary

199000.0

Posted On

13 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

C#, Java, Python, Machine Learning, Generative AI, Natural Language Processing, Deep Learning, MLOps, Azure, AWS, GCP, TensorFlow, PyTorch, Data Processing, Docker, Kubernetes, Algorithm Design

Industry

Software Development

Description

Overview This is an exciting time to join our group Customer Experience Engineering and work on something highly strategic to Microsoft. The goal of the Customer Experience Engineering is to build the next generation of our applications running on Dynamics 365, AI, Copilot, and several other Microsoft cloud services to deliver high value, complete, and Copilot-enabled application scenarios across all devices and form factors. We innovate quickly and collaborate closely with our partners and customers in an agile, high-energy environment. Leveraging the scalability and value from Azure & Power Platform, we ensure our solutions are robust and efficient. If the opportunity to collaborate with a diverse engineering team, on enabling end-to-end business scenarios using cutting-edge technologies and to solve challenging problems for large scale 24x7 business SaaS applications excite you, please come and talk to us! We are looking for a Software Engineer II (AI/ML) to join us. This position is based at the Redmond campus with 3 days per week work in the office and 2 days per week work from home. Relocation assistance is available. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities Design, develop, and implement robust and scalable software applications that utilize generative AI techniques. Integrate LLMs and NLP into software solutions for tasks such as content recommendations, summarization, translation, and retrieval. Optimize generative AI models and their performance for specific use cases. Help establish and drive the adoption of good coding standards and patterns. Help identify opportunities to improve and optimize existing systems using generative AI. Stay up-to-date with the latest trends and technologies in generative AI. Embody our Culture and Values Qualifications Required Qualifications: Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C#, Java, or Python OR equivalent experience. Additional 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: Knowledge of machine learning algorithms and concepts (e.g., supervised learning, unsupervised learning, deep learning) as applied to generative AI. 2+ years of professional experience in a technical role developing, training, evaluating and deploying ML solutions at scale for real-world problems Familiarity with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, Keras, etc. Experience in handling large datasets and working with data processing frameworks (Apache Spark, Hadoop etc.) Hands-on experience with cloud platforms like Azure, AWS or GCP for deploying and scaling machine learning models. Experience in designing and implementing MLOps strategies for model deployment, monitoring, and governance. Familiarity in deep learning architectures (Transformers, CNNs, RNNs) and knowledge of Natural Language Processing (NLP) Knowledge of containers (Docker) and orchestration tools like Kubernetes. Solid computer science fundamentals in algorithmic design, data structures, and analyzing complexity. Ability to balance competing demands and adapt to changing priorities. Experience mentoring early-in-career engineers and data scientists, providing technical guidance and code reviews. Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $131,400 - $215,400 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

Design, develop, and implement robust and scalable software applications that utilize generative AI techniques. Integrate LLMs and NLP into software solutions for various tasks and help optimize generative AI models for specific use cases.