Address Overview
From being able to log you in with face recognition, correct your gaze during a video chat to maintain eye contact, to the exciting new opportunities emerging from computers’ understanding of natural language, are you itching to play a part in bringing AI applications to millions?
The Microsoft Applied Sciences Group incubates disruptive technologies for Microsoft’s next-gen hardware products and is working on several exciting projects that will shape how computers and other devices perceive the user and the user’s environment. Operating as a startup within the company, this team works closely with several research and product teams to bring compelling new experiences to the market. A lot of these experiences will rely on a combination of cutting edge hardware and cloud technology. As part of this team, you will have the unique opportunity to develop novel software solutions for different platforms that will revolutionize how people use computers in the future. Join our dynamic team, where you'll be entrusted with the responsibility of turning sophisticated machine learning models into optimized applications and APIs. With the rapid evolution of the AI ecosystem, each day brings unique challenges. Your expertise in Machine learning, specifically in the field of large language models, combined with your keen analytical skills, will be instrumental in navigating these challenges. Collaborate closely with a diverse, high-calibre team in a high-energy environment to deliver top-notch, maintainable solutions.
Responsibilities
Responsibilities
- Contribute to innovations in Large Language Models, AI, Machine Learning, Computer Vision, and Deep Learning.
- Work on generating data for Large LMs using efficient prompting techniques used for model finetuning.
- Evaluate, compare, deploy large models for deployment on power efficient hardware and ship solutions for customer.
- Collaborate with cross-functional teams across Microsoft to define and refine next-gen AI applications.
- Assist in identifying and addressing issues in ML frameworks and associated hardware.
- Mentor junior engineers and contribute to team knowledge-sharing sessions.
Qualifications
Qualifications
- A Ph.D. in Computer Science, Math, Physics, Statistics, or related areas or Candidates with master’s degree with 3+ years of relevant industry experience
- Minimum 3 years of experience in deep learning and its different toolkits particularly Pytorch or Tensorflow
- Minimum 3 years of Python and C++ (especially C++11 and newer) experience.
- Record of publications in top-tier conferences or journals (ICML, CVPR, ICCV, ECCV, NeurIPS, ICLR)
- Experience of dataset generation, curation, post processing and evaluation of large models.
- Familiar with Language models, transformers like BERT, GPT-2/GPT-3, Llama, OPT etc
- Experience in model quantization & optimization techniques such as GPTQ, LORA etc.
- Experience of using dataset curation, data generation using prompting state of art LLMs, automated model evaluation.
- Experience optimizing large language models, model compression, network distillation into small language models.
- Experience with Parameter-Efficient Fine-Tuning methods for foundational models.
- Experience with model conversion and deployment frameworks like ONNX is a plus.
- Experience in network architecture search, quantization, is a plus.
Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
