Student Incubator

Kick-start your career in data science, with
personal mentorship and hands-on industry experience.


Our student Incubator offers a unique mix of academic mentorship, hand-on project work and technical training. It is a highly selective program where you will be mentored by an industry expert as you develop a pragmatic solution to a real-world problem using machine learning.

Whether it is your thesis or a project you’re passionate about, you will complete the program a more confident coder, make invaluable industry connections and gain a wealth of practical learning which may be applied anywhere you work.

Additional details on the program.

We are currently not accepting new Incubator students.

Read about the exciting GSoC 2017 journey from our students ParulPrakhar and Chinmaya.

Parul SethiParul Sethi
Beautiful visualizations for topic models and practical training statistics in Gensim.

Prakhar PratyushPrakhar Pratyush
Hardcore performance improvements to collocation detection and fastText in Gensim.

Chinmaya PancholiChinmaya Pancholi
Gensim integration with Scikit-learn and Keras.


No cost to participate

Open to students at both partner and non-partner universities

8-40 hours/week (8 hours is the bare minimum; more time is typically required.)

Remote; work from anywhere

1-2 semesters


The Incubator is open to anyone, but we also keep long-term collaboration with select institutes. Is your organization interested in partnering with RARE?
Contact us today to discuss.


Ólavur Mortensen

Master’s student in Applied Mathematics focused on machine learning, NLP, and text mining.

Šimon Pavlík

Graduate student at the Czech Technical University in Prague, majoring in Artificial Intelligence.

Bhargav Srinivasa Desikan

Machine Learning researcher currently working at INRIA Lille-Nord. I enjoy contributing to Open Source Scientific Computing and thinking of ways you can use Computing to better understand the humanities.

Devashish Deshpande

Undergraduate student in CS at BITS Pilani University in Goa, India, interested in NLP. I enjoy contributing to different open source projects. I have a great interest in music, football and tennis.

Hayate Iso

Researcher at Nara institute of science and technology (a.k.a. NAIST) Enthusiastic about statistics and natural language processing.

Jayant Jain

IIT-R graduate in Mechanical Engineering, experienced in web development. Generally curious about technology, working on NLP and text analysis.

Rishab Goel

Student at IIT Delhi Masters programme in Computer Science. I like to use Machine Learning and NLP for real life problems like Recommender Systems.

Mridul Seth

I am a student at BITS-Bilani. Looking forward to work on language modelling using recurrent neural networks with the gensim open source Incubator.

Haoming Jiang

Student at the University of Science and Technology of China. The Student Incubator Project is a great opportunity for me to contribute to the data science community with some awesome Gensim techniques.

Daniel Roudnitsky

Student at Brooklyn Tech High School in Brooklyn, NY. I worked on Approximate Nearest Neighbours library integration and improving word2vec. Interested in continuing to work on Neural Networks.

Parul Sethi

Undergraduate student of Maths and IT at CIC, University of Delhi. Interested in mathematical sciences and like contributing to scientific computing projects.

Mohit Rathore


IIT(ISM) – Dhanbad under-graduate. Interested in almost all domains related to machine learning. Currently researching in natural language processing and artificial general intelligence.

Partho Mandal

Masters Student in Computer Science at Arizona State University. I’m very excited to be actively enrolled in the RaRe Incubator program involved in development of word2vec visualization

Anmol Gulati

Graduate student at IIT Kharagpur majoring in Mathematics and Computer Science, Former ACM-ICPC World Finalist ’15, ’16, Robot-soccer enthusiast and a Quintessential thinker.

Aaditya Jamuar

Undergraduate Student at Manipal Institute of Technology, India. I enjoy using power of computing to help human decision making. I have done multiple projects on web technologies, data science, text mining and machine learning.

Aayush Yadav

Aayush Yadav is a Computer Engineering undergraduate from Pune, India.

Aman Gill

Aman Gill is an undergraduate student interested in Machine Learning and NLP.

Pranay Mathur

Pranay Mathur is a Data Science enthusiast from University of Pune.

Shiva Manne

BITS Pilani graduate with Masters in Economics and Bachelors in Computer Science. Machine Learning enthusiast and a huge Deep Learning fan.

Chaitali Saini

Undergraduate student at Cluster Innovation Centre, DU majoring in IT and Maths. Interested in NLP and likes contributing to open source.

Xiaohong JI

Student at the Wuhan University. Interested in machine learning and deep learning.


  • Learn from industry experts
    Collaborate with industry experts in data science and machine learning who are ready to share their insights, review your code and invest in your professional development.


  • Gain invaluable hands-on experience
    Apply what you’re learning in practical ways, test your ideas and work through problems beyond classroom theory.


  • Receive scholarships and financial rewards
    The most talented students are eligible to receive scholarships and financial compensation for completed projects. Non-partner students may also be compensated on a case-by-case basis.


  • Connect with career opportunities
    Buff up your résumé with a verifiable trail of accomplishment. Successful Incubator students are considered for collaboration on paid, commercial work with RaRe Technologies – the ultimate way to fast-track your career.


  • Improve the world through open source
    Make a meaningful contribution to the open source community with the help of an experienced and passionate mentor.



At RaRe, I worked with Recurrent Neural Networks. The work is very creative, experimental and scientific, yet at the same time very practical.
I experienced the work-flow of a project with many people involved. I learned how to make my work results more formal, reproducible and easily usable by other people.

Šimon Pavlík, Czech Technical University in Prague


No university projects will give you the kind of real world experience you will get here.
I have a background in applied mathematics, and one thing that I learned a lot from was doing software development, using GitHub and collaborating with people. Having someone looking very critically at my code was very beneficial for me. I also appreciate getting to write tutorials for the RaRe Technologies blog.

Ólavur Mortensen, Technical University of Denmark


This is an amazing opportunity, but it isn’t for everyone.
While there is no cost to participate, RaRe invests serious time and attention into our Incubator students and their projects,
and we expect you to do the same.

Here’s what we’re looking for:

  • You are a bright and dedicated student of data science
    You’re passionate about machine learning and you’re prepared to go beyond classroom theory to build pragmatic solutions. Successful applicants are typically working towards their MSc or a PhD in a related field.
  • You’ve got coding skills, and you’re not afraid to use them.
    You know your languages, mind the details and always look for a more efficient way to do things. You’re already applying your skills by contributing to the open source community.
  • You’re self-driven and internally motivated.
    You take it upon yourself to investigate new ideas and have no problem holding yourself accountable to a deadline. You’re prepared to put in your best work, knowing that this is what’s expected of you.
  • You’re a strong communicator and thrive on collaboration.
    You thrive on constructive feedback and can express yourself clearly in writing. You’re ready and able to attend weekly progress meetings and contribute your thoughts to our discussions.

This program is NOT for you if…

  • You are “just curious” or want to “give it a try.”
    If you’re not certain that a career in data science/machine learning is for you, this program won’t help you decide. Likewise, this is not a “free ride” or a program you “try out”. It’s an intensive but rewarding experience that you’re either ready to commit to, or you’re not.
  • You struggle with time-management.
    We’re here to mentor you – not hold your hand. Deadlines are not suggestions and meetings are not optional. If you can’t work independently, please don’t apply.
  • You cannot commit to 8-40 hours/week.  
    We want to maximise the value you get out of this experience, and that means committing to making steady progress. If you rush your code or try to pull an all-nighter, it will show in your work.


We collaborate with students on both general open source and thesis projects.
You’re welcome to choose a thesis from our repository of available thesis projects, or bring your own project idea for evaluation.
Projects typically last 1-2 semesters and require 8-40 hours per week to complete.
Please note: We do not collaborate with students on commercial or client projects while they are in the Incubator program.


If you’ve reviewed our expectations and you’re confident you can meet the program’s mandatory time commitment, please send us:

  • A cover letter
    Share who you are, why you’re a fit for the program and why you want to be involved in the program.
  • Your academic affiliation
    Tell us where you study. We welcome qualified students from both partner and non-partner universities.
  • A summary of your skills
    Outline your relevant experience, including the programming languages you know and any projects or open source contributions you’ve completed in the past. If you have one, a link to your GitHub would be great to see.
As part of the admission process, you’ll be required to complete a short open source project.

Think of it like an entry exam: we want to see your skills in action and make sure you’re up for the challenge.

If successful, you’ll be admitted to the Incubator and the mentorship will begin.

Once approved for admission, you’ll choose a full project to focus on.

This can be part of (or grow into) a full academic thesis, or it may simply be a project you are passionate about. You can select a project from our available thesis projects, or present a machine learning idea of your own.

After setting milestones for completion, we’ll connect on weekly progress meetings, code reviews and discussions to offer advice and make sure you’re on track.

Apply Now

Questions about the Incubator?

Contact [email protected]