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Machine Learning Advanced Accelerator Program
Factored
Latin America
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Who we are:
Factored 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, and data science engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are committed to vetting, selecting, and nurturing the most talented engineers on behalf of our clients.

About The Training Program:
We are bringing the best educational resources from Silicon Valley to Latin America. We have successfully completed Machine Learning Engineering and Data Engineering Programs in the past and we are glad to launch another Machine Learning Engineering Advanced Accelerator Program (AAP).  

Our recruiting process is highly selective; we’re always looking for the best talent. Our immersive, full-time advanced training in Machine Learning is a 12 weeks program. Upon completion, we offer selected trainees full-time job opportunities to work on cutting-edge data-driven projects and continuous learning paths for Factored employees to constantly update their skills. Better still, the advanced accelerator program comes with no upfront costs for participants, and we even pay a monthly stipend to cover living costs.

What you can expect:
Members of our AAP team include: AI experts, educators, and industry AI leaders. We’ve designed a curriculum that aims to make engineers industry-ready in 12 weeks. ​The Machine Learning Engineering AAP comprises 5 components:

1. Online content with extended discussions, assignments and weekly projects.
2. Guided hands-on projects based on state-of-the-art approaches to ML.
3. Capstone projects based on challenging real-world problems.
4. Workshops and talks.
5. Mentorship and soft-skill development.

The AAP covers the technical challenges behind AI, from the algorithmic foundations to the computational challenges of building and scaling successful AI systems in real-world problems. #LI-Remote

Who should apply to the ML advanced accelerator program?

    • People with the desire to boost their careers in the Machine Learning engineering field. You might be a good fit if you have at least 1+ years of experience working in the data field as a: data scientist, machine learning engineer, or backend developer.
    • Bachelor’s degree in Computer Science, Software Engineering, Mathematics, Economics or other engineering fields.
    • Demonstrated interest or experience in AI.
    • Strong Math, Calculus, Linear Algebra, and Statistics foundations.
    • Proficiency in any programming language (preferably object-oriented).
    • Excellent communication skills in English.
    • Strong problem solvers and quick learners.

When does the ML advanced accelerator program start?

    • Starting date to be defined. You will receive all the information and updates by email. Ideally one APP per semester.
    • The program will run for approximately 12 weeks.
    • This is a fully remote program.

How much does the ML advanced accelerator program cost?

    • Factored does not charge upfront; we pay a monthly stipend for our scholars, and if they successfully complete the program, we offer them full-time employment. When joining the advanced accelerator program, you will agree to sign a blank promissory note with the equivalent amount of the program investment. However, if you remain a Factored employee for 2.5 years, the entire training cost is considered to have been repaid.

What is the admission process?

    • Candidates need to complete an online application.
    • Candidates who pass the resume screening will receive two invitations by email to complete two different tests: Test #1 Algorithmic Coding & Test #2, Mathematics & Statistics.
    • Candidates who pass the assessment stage will receive instructions for the behavioral and tech interviews.
    • Candidates who pass the above stages will receive instructions for the cultural fit interview with our CEO, Israel Niezen.
At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.  

We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth is available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business, but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business, and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts. 

 In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission.  When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.
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