Machine Learning Engineers Training Program
Training Program /
Who We Are:
Factored (an AI Fund Portfolio company) was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.
About The Training Program:
We are bringing the best educational resources in AI from Silicon Valley to Latin America. We have already graduated three cohorts and will be launching another in July 2021. Our recruiting process is highly selective, always looking for the best talent. We provide a 3-month full-time training in AI and offer selected Trainees full-time job opportunities to work on cutting-edge AI projects. Even better, the training program comes with no upfront costs and we pay a monthly stipend to cover your living costs.
What You Can Expect:
Our team is composed of the leading AI experts, educators, and industry AI leaders. We’ve designed a curriculum that aims to make engineers industry-ready in 3 months. Training is composed of 3 phases:
1. Online content with extended discussions, assignments and weekly projects.
2. Literature review and paper implementation.
3. Capstone projects based on real-world problems and datasets using development good practices.
Our program is optimally designed to cover the technical challenges behind AI, from the foundations behind learning algorithms to the computational challenges to build and scale successful AI systems in real-world problems.
Who should apply to the training program:
- Bachelor's degree in Computer Science, applied Mathematics, Data Science, Economics or other engineering fields.
- Proven passion for AI/ML/DL/Data Science, reflected in any of the following: work experience, personal projects, theses, articles, publications, conferences and other ML related projects.
- Strong Math, Calculus, Linear Algebra and Statistics foundations.
- Strong problem solvers and quick learners.
- Msc or Higher is a plus.
- 2+ years of working experience (DS, SW Engineering, Data Analytics, Researching).
- Outstanding communication skills in English.
- Proficiency in any programming language. Python is a plus.
When does the training program start:
- It starts in July 2021 and runs for approximately 3 months.
- Remote training program.
How much does the training program cost?
- Factored does not charge upfront, we pay a monthly stipend for our scholars, and if they pass our course, we offer them full employment. By joining the training program, you agree to sign a blank promissory note with the equivalent amount of the training program investment. However, if you remain a Factored employee for 3 years, the entire training cost is considered to have been repaid.
What is the admission process?
- Candidates need to complete online application by May 12th.
- Candidates who pass resume screen will receive an online technical test.
- Qualified candidates will receive instructions for tech-interviews.
At Factored, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants without regard to race, color, religion, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, and any other characteristic protected by applicable law. AI Fund believes that diversity and inclusion among our employees is critical to our success as a company, and we seek to recruit, develop and retain the most talented people from a diverse candidate pool. Selection for employment is decided on the basis of qualifications, merit, and business need.