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About the Program

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. 

We’d love to hear from you if:

- 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.

Eligibility Requirements

- 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.

Eligibility
Qualifications

Qualifications

  • A mathematical, statistical and probability inclination and deep understanding of some AI concepts. 
  • Hands on coding with Python or any proficient AI language of choice.
  • Familiarity with NLP/ML tools and packages like Keras, Caffe, pyTorch, TensorFlow, scikit-learn, nltk
  • Proficiency in machine learning algorithms such as multi-class classifications, decision trees, support vector machines, and deep learning
  • Strong understanding and experience with  different types of neural networks (ANNs, CNNs, RNNS, Auto-encoders, Variations Auto-Encoders, Transformers, etc)
  • Strong understanding of probability and statistical models (generative and descriptive models)
  • Ability to run experiments scientifically and analyze results
  • Ability to effectively communicate technical concepts and results to technical and business audiences in a comprehensive manner.

Responsibilities

  • Collaborate with the AI team at Infrrd to bring product vision to life.
  • Designs and develops scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements
  • Creates metrics to continuously evaluate the performance of machine learning solutions
  • Training and fine-tuning the models
  • Keeps up with new tools, algorithms and techniques in machine learning and AI to implement them in the organization
Responsibilities

Question & Answers

Who should apply to the Infrrd AI Residency program?

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.

What can I expect from the program?

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.

Where is the Residency Program based?

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.

How do I apply and what does the application timeline look like?

Infrrd welcomes applications year-round.

I’m interested in applying! What documents do I need to prepare in order to submit my application correctly?
Cover letter

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.

Letter of Recommendation

Your recruiter will let you know if a letter of recommendation is required and the deadline for submitting it. 

Transcript

Please submit a current or recent unofficial or official transcript in English. 

What does the program curriculum look like?

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.

Any resources you can recommend to help me prepare for the interviews?

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:

  • Andrew Ng's original ML Coursera class + his more recent series of deep learning courses
  • Michael Nielson's deep learning textbook
  • Chris Olah's blog 
  • Ian Goodfellow's Deep Learning textbook 
How does the project assignment process work?

Project assignment will take place shortly after your residency begins. This will happen based on your skillset, interests and the availability of projects.

What will I learn (What you will get out of the program)

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.

Project Topics

Context-Learning

Context Learning

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

Reinforcement learning

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. 

Trainable-model

Trainable model (N-shot learning)

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-scoring

Confidence scoring

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.

Find your dream job.

Make it happen at Infrrd.
Search Our Open Positions

Who is Infrrd?

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™.

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