- Use Cases
Looking to play an integral role in developing algorithms, product development, patents and more? Infrrd’s Residency offers you a chance to spend 12 months building features that will be integrated into our system. As a Resident, you’ll be on-boarded as a fulltime employee for the duration of the year. You will develop, build and optimize machine learning, computer vision, natural language and perception algorithms for Infrrd’s Intelligent Data Processing platform. Unlike with an internship, which lasts just a few months, you’ll get to see your work in action, as you build features directly used by existing products.
- You are a team player who gets inspired by your peers -- at all levels -- and enjoys collaborating to build the best product possible.
- You see complex projects as a chance to learn and grow, and love a good challenge.
- You’ve got a knack for diagnosing complex technical issues, and are skilled at supporting hardware and software solutions.
- You are driven by curiosity, and don’t let ego get in your way.
- BS, MS, or PhD in a technical field or equivalent experience
- Coursework in or working knowledge of linear algebra, calculus, and probability
- Coding ability in Python
- Demonstrated experience with machine learning or deep learning algorithms, and demonstrated passion for the same.
If you’re passionate about machine learning and motivated to learn, this could be the right program for you. An MS or PhD degree is preferred but not required. Applicants without an MS or PhD degree but with relevant STEM experience and research interests will also be considered.
You’ll be reading papers and doing projects that will build out your machine learning research skills, with an eye toward publication. As with advanced studies programs (Master’s/PhD), you’ll build a strong background in machine learning.
Infrrd’s AI Residency Program is primarily based in San Jose, California, but residents have the option of being based in the United States or India depending on project fit and team needs. Your recruiter will work with you to determine the best location for your work, once pandemic restrictions are lifted.
Infrrd welcomes applications year-round.
Infrrd recruiters will use cover letters and resumes to screen applicants. Please make sure to include both technical and non-technical information, as well as to highlight any relevant past research experience.
Your recruiter will let you know if a letter of recommendation is required and the deadline for submitting it.
Please submit a current or recent unofficial or official transcript in English.
You will focus on a project related to one or more of the concepts below. As a confirmed program participant, you will receive a list of relevant courses tied to your skills and interests to aid you in your project work.
We recommend a few courses, sites and books you can use to review and practice for your interviews for the AI Residency Program.
Machine Learning & Deep Learning:
Project assignment will take place shortly after your residency begins. This will happen based on your skillset, interests and the availability of projects.
You’ll work with our engineers to develop multidisciplinary algorithms that will be used in the final product.At the end of our one year program, we hope to equip you with a highly specialized knowledge of developing reliable, scalable and optimized algorithms to solve complex real world Artificial Intelligence problems.
Documents’ meanings change drastically based on context. We make use of NLP and layout to try and replicate how our brains map that context. Instead of memorizing the values, our models need to be able to generalize the values from other documents and surrounding text.
Reinforcement learning teaches algorithms based on penalty and reward. We are committed to developing tools to put reinforcement learning to work in the IDP space to help eliminate the need for training data.
Right now, machine learning models learn with lots of data. The more data we feed the system, the better it performs. We want to figure out how to teach our models to perform better using less data.
Confidence scores are based on understanding. We want to help models reflect meaningful confidence scores that compensate for the fact that the model could be looking at a narrow band of knowledge to generate its assumptions.
In a fast-paced world filled with never-ending rivers of documents and data, organizations continuously need smarter ways to work. Teams need flexible solutions that enable them to work faster while delivering higher levels of reliable accuracy than ever before. At Infrrd, we empower teams with
Intelligent Document Processing Solutions for Intelligent Work™.