Software Engineer

As a part of Microsoft AI platform responsible for providing an efficient runtime and end-to-end application for state-of-the-art (SOTA) AI models that powers the world both within (including co-pilots) and third party applications. 

Our team delivers scalable LLM (Large Language Models) inferencing in cost efficient manner for speech, vision and text (multimodal) inputs. Our services are based on Kubernetes based scalable compute on both CPU and GPUs. We also deliver SOTA performance in delivering (near-) real time model personalization.  

We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served.  

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.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Required/Minimum Qualifications  

• · Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

  o OR equivalent experience.

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/Additional Qualifications:  

• Ideal candidates will have AI, Machine Learning or any one or more of speech, vision or NLP background.  

• Experience in benchmarking and optimizing algorithm execution on GPUs. 

• Experience with Nvidia, AMD and FPGA type hardware is a plus. 

• Excellent written and verbal communication skills  

• Results-oriented free thinkers with ability to socialize and drive their ideas into technology.   

Software Engineering IC2 - The typical base pay range for this role across the U.S. is USD $81,900 - $160,200 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 $105,600 - $174,600 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

Microsoft will accept applications for the role until October 21, 2024.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

#AIPLATFORM

Design and implement high performance microservice components, algorithms and integration with other services to deliver scalable API, offline containers as well as libraries for Microsoft Office and Azure Open AI services.  

• Innovate and bring cutting edge solutions to run AI models at scale ranging from core runtime enhancement using batching, intelligent data packing, parameter efficient inferencing, orchestrating between different AI components. 

• Trouble shoot system level live services, offline container performance across data centers 

• Improve the quality of model inference by reducing latency, running models on novel human-like neural network engine as well as traditional GPUs 

• Implement telemetry and logging to measure performance metrics on GPUs, define new metrics and optimize model inferencing