We have developed this resource to support early career researchers working at the intersection of AI and physics to understand what opportunities for internships and jobs in industry may exist and collect advice on exploring those opportunities. We are crowd sourcing this information from IAIFI members and alumni and would appreciate your help! If you have experience working either an internship or job in industry, please share and help fellow researchers learn about a variety of opportunities and work environments.
Organizations with IAIFI Connections
We are currently working to build a database of industry connections and opportunities as we gather information from more members–please share your experience to help us complete the database! If you have questions about contacting any of the organizations above, please email iaifi@mit.edu.
Finding jobs/internships
There are countless ways to identify potential jobs and internships. Some ways that IAIFI members and alumni have had success with include:
- Discussions with their advisor about their career interests. Faculty members then helped point them toward opportunities and connected them with people in their network.
- Career Fairs such as the MIT Analytics Career Fair, which provide opportunities to meet with representatives from companies looking to hire.
- Taking advantage of MIT Career Advising & Professional Development resource for faculty, PhD students and postdocs only.
- Following companies and researchers in industry on Twitter, where they often will post job opportunities.
- Taking advantage of networking at conferences and staying in touch with connections.
Application advice
- Mention researcher at the company who works on relevant topics to your research
- Include a short description of what you would like to work on in your application
- Know how to explain your work to a layperson knowledgeable in data science and machine learning; practice talking about research in compelling ways and make sure that this is reflected in your application materials
- Practice thinking about how you would implement an ML solution in business (e.g. a movie suggestion algorithm for Netflix)
- Consider how your implementation choices can affect different stakeholders and other parts of the business
- Have a well-organized repo with high-quality code samples
- Focus on doing cool/interesting research and building useful skills in your PhD
- Have someone from industry read over your application materials, they are very different from academia
- Reach out to people, in particular if you met them at conferences
Interview advice
- Have a rough idea of the research you’d like to work on
- Practice Leetcode questions and explaining your work
- Show that you can work well in a team in a fast-paced environment and can write good code and maintain a reasonably sized codebase
- Do a lot of interview prep until you feel confident doing any LeetCode problem while someone is watching (and judging); you also want to be able to do some basic stats and probability on the fly.
- Be prepared to talk about your research in compelling ways
- Leetcode is good to prepare the coding aspects, but often times machine learning code interviews will be more conceptual and they’ll just look for clean code (mention testing for instance and write tests if applicable, also read PEP 8)
- Make sure that you can bring up an exciting project that they’ll understand, the interviewer may just focus on that project and ask you questions about it
Influence of experience on career goals
- Broadened ML knowledge, which will be useful in future career, and will be more open to work in industry in the future.
- Made applying for academic jobs less stressful!