MAIDAP

Offering new graduates the opportunity to collaborate with product groups across Microsoft by tackling some of our most exciting and challenging AI problems.

Join us on the leading edge of AI

To foster Microsoft’s leadership in the field of AI, we created this ground-breaking program to develop the next generation of AI leaders. The Microsoft AI Development Acceleration Program (MAIDAP), located at the Microsoft New England Research and Development Center (NERD), is an early-in-career program for recent BS/MS/PhD graduates to gain exposure to a diverse set of AI opportunities at Microsoft.  Prospective members may join as a program manager, software engineer, or data scientist, and work closely together to launch AI products that can impact billions of end users.

Every summer, a new cohort begins a two-year rotation program.

Candidate Qualifications

Graduates with a PhD/MS/BS degree in Electrical Engineering, Computer Science, Data Science, Statistics or other relevant fields, and any of the following qualifications:

  • Analytical and problem-solving skills
  • Organizational and leadership skills to work in a highly collaborative, interdisciplinary environment
  • Experience with machine learning techniques or deep learning frameworks is preferred but not required

We are currently hiring for the following positions:

Director's Message

In 2018, we launched a ground-breaking program to develop the next generation of AI leaders at Microsoft. Located at the New England Research and Development Center (NERD) in Cambridge, Mass., the Microsoft AI Development Acceleration Program (MAIDAP) attracts recent graduates from BS, MS, and PhD programs in Machine Learning and Artificial Intelligence-related fields.

Working collaboratively with our product teams, these innovators address some of the most exciting and challenging AI opportunities at Microsoft and have already made significant contributions to further Microsoft’s leadership in the areas of Intelligent Cloud, Intelligent Edge, productivity, and Responsible AI.

FAQs

Who is eligible to enter the program?    

The MAIDAP Program is targeted at graduating students from BS, MS, and PhD programs. Students who graduated more than a year ago are outside the target candidate pool.

When does the program start and what is its duration?

A new cohort starts every summer, and the program lasts for two years.

I have a visa-related need to start early. Is that feasible?

We will process such requests on a case-by-case basis.

Where is the program located? 

Our entire team sits together at our Cambridge, MA location. We overlook the Charles River with a wonderful view of the Boston skyline.

Are these roles research oriented or industry oriented?

While many of our projects are launched or shipped to external customers, we also embark on projects that extend the state-of-art AI systems in ways that are likely to impact our customers in the future.  Often, MAIDAPers are found applying cutting-edge research to high-scale products via new innovations.

What happens at the end of the program?  

Cohort members are full-time employees from the start of the program and are seamlessly incorporated into one of the sponsoring teams.  This is accomplished via a mutual matching of interests and needs.

What makes MAIDAP unique?

As a part of MAIDAP, you will work on cutting-edge, high-visibility AI problems in deeply multidisciplinary teams of program managers, software engineers, and data scientists.  The guidance and learning that comes with each rotation spans real-world product development, model deployment, management, explorations, data storytelling, and critical thinking – all key skills that will help you skyrocket the rest of your career.  In addition to building out core skills, MAIDAP has a vibrant culture – we celebrate the diversity of our cohort members in background and perspectives.  At MAIDAP, all voices are included when innovating on the next big thing.

 

What kind of projects does MAIDAP work on?

MAIDAP projects span a wide range of fields such as NLP, Vision, IOT, Telemetry, Data, Reinforcement Learning, among others.  We partner with organizations such as Office, Teams, Azure, and Windows to accelerate their products through AI/ML.

How do MAIDAPers stay connected with the AI/ML community?  

In addition to participating at major AI/ML conferences, MAIDAP members often disseminate learnings by writing papers and communicating the impact of our work with the community.

How does the program support the learning and growth of cohort members?

MAIDAP and Microsoft have a steadfast commitment to your career growth.  Through the strong community that the program provides, MAIDAPers are supported with professional development resources and mentorship throughout their rotations.  Furthermore, MAIDAP members are set up to become entrenched in the field of AI by receiving support to attend major AI conferences, presential and remote classes, and a host of internal reading groups across Microsoft teams.  Through these experiences and opportunities, members are equipped with a unique toolkit to tackle the world’s most impactful problems in AI/ML.

Projects

Here are some of the public-facing MAIDAP projects our cohort has been working on.

MFA Project. GAN 

Microsoft, MIT, and the Metropolitan Museum of Art (the MET) collaborated on a project to bring the MET’s recently open sourced art collection to a wider audience by developing interactive engagements using AI methods. This collaboration is best described in The MET’s articleThe MET’s blog, and Microsoft’s blog.

The MAIDAP team saw a fantastic opportunity to leverage our information extraction pipeline, improve it by experiencing it as a user, and prove its efficiency with an extremely rich dataset. In the course of a hackathon, we created and trained a generative adversarial network capable of transitioning smoothly between visual states closely resembling artwork of the MET.

NLP Repo

MAIDAP, along with the AI CAT and AzureML teams, has recently launched an open-source GitHub repository on Natural Language Processing (NLP). This repository received over 1,300 stars and was forked over 100 times in its first two weeks of availability. The included tutorials and examples ensure that our customers can use state-of-the-art algorithms for common NLP scenarios and easily build AI solutions on Azure. The repository contains a set of Python notebooks grouped by scenario, including Sentence Similarity, Question Answering, Text Classification, and Entailment. These scenarios use open-source libraries such as BERT and AllenNLP to teach common NLP concepts and best practices for implementing NLP solutions on the Azure ML infrastructure.

XLNet Starter Kit

NLP is one of the main domains in Machine Learning and with its ever-changing landscape it is often difficult to keep up with the state-of-the-art models. We created an easy to use template for one such model, XLNet. XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. We created an easy to use Jupyter notebook that anyone can use to perform text classification using XLNet. The project was added to NLP Best Practices repository and the boilerplate notebook now lives here.

Bike Parking Predictions

Our project is a website that predicts the number of bikes in the parking garage at NERD at any time. We experimented with multiple methods for measuring the number of bikes, including a camera running a bike detection model. Eventually, we decided to use RFID data from the building to count the number of entrances and exits from the garage. In addition to RFID data, we used weather data, including precipitation, humidity, temperature, and other factors to train different models. Our best model is a Lasso Regression model that achieves an R squared of 88%.

Project Buzz Aldrin: AI as your partner in gaming

The goal of the project was to build a cooperative AI which assists people with disabilities to play XBOX games thereby enhancing the gaming experience of the consumer. Consequently, the human and AI work together to win the game using the best possible strategy. The team used Reinforcement Learning (RL) to develop six possible Human-AI collaborative environments. A user study was conducted to determine which of the six methods is the best approach for human-AI collaboration. This project strongly ties into the accessibility element of the Microsoft culture.

BERT Notebook

Our project goal was to accelerate the ability of data scientists without a strong natural language processing (NLP) background to learn NLP best practices. Microsoft’s new open source NLP repo contains examples for building state-of-the-art NLP systems, many of which were built by MAIDAP. Using these notebooks as a starting point, we investigated whether we could effectively train BERT (Bidirectional Encoder Representation from Transformers) for sentence similarity.

DeepMurals

DeepMurals is inspired by a popular technique called NeuralStyle Transfer first introduced in a paper by Gayts and his team. This project aims to recreate an image in the style of another image of one’s choosing. Developed using an enhanced version of the original implementation, the project extended the scope from style transferring images to style transferring videos in near real-time. By pretraining Style Transfer algorithms on GPUs, Neural Style Transfer becomes accessible to everyday scenarios at the click of a button. Accessible Style Transfer can lead to innovations in interactive artistic experiences, with the potential to be integrated into museums and exhibits.

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