Advanced Accelerator Program (LLMs & MLOps Specialization)

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 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.

Our Program:

Our Advanced Accelerator program focuses on guiding engineers to the pinnacle of their careers, whether in LLMs or MLOps. It outlines a robust progression path that develops deep expertise in these essential domains of AI, driving engineers from foundational knowledge to mastery. Through our focused learning objectives and hands-on experience, we equip engineers to design, build, and maintain cutting-edge ML systems and large language models (LLMs).

Choose Your Path to Engineering Mastery:

Engineers ready to advance their careers can specialize in either LLMs or MLOps, while developing strong foundational knowledge in software and ML systems design. By specializing in one of these fields, you can accelerate your career and make a meaningful impact on the future of AI technology.

During the selection process, our training team will guide candidates in choosing the best specialization path tailored to their skills and goals.

LLMs Specialization:

Our LLM-focused progression path equips engineers with the skills needed to design, optimize, and deploy LLMs in real-world applications. It emphasizes critical aspects such as guardrails and robust evaluation frameworks to ensure safe, reliable, and high-performing models.

The complete syllabus will be shared with the applicants during their selection process.

MLOps Specialization:

MLOps is the backbone of every high-impact machine learning system, ensuring that models are not only built but also deployed, monitored, and continuously improved in production. Our MLOps progression path is designed for engineers eager to master the end-to-end lifecycle of machine learning platforms, from experimentation to serving and beyond.

The complete syllabus will be shared with the applicants during their selection process.

By the end of the advanced accelerator program, both LLM and MLOps engineers will be able to design scalable, high-performance ML systems, balance competing objectives like cost and performance, and develop end-to-end solutions that integrate data pipelines and deployment. You'll also communicate complex designs through visual tools and apply best practices in software engineering to build reliable, production-ready systems that meet business needs.

Qualifications:

• At least 3 years of experience in the machine learning field, with a proven track record of implementing ML models or solutions.
• At least one year of NLP experience (for candidates choosing the LLM path) or one year of experience in MLOps (for candidates choosing the MLOps path).
• For the LLM path: Experience using transformer architecture models for NLP tasks and familiarity with PyTorch/TensorFlow and Hugging Face.
• For the MLOps path: Familiarity with ML model deployment, CI/CD pipelines, and tools like Docker and Kubernetes.
• Understanding of machine learning algorithms, data pipelines, and software engineering principles.
• Proficiency in production-grade Python code and basic understanding and experience in Backend Development.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Ability to communicate technical concepts clearly and collaborate effectively with cross-functional teams.
• Excellent written and spoken English communication skills and the ability to have in-depth technical discussions with both the engineering team and business people.

Program Logistics:

• Start date: February 2025.
• Type of program: Full-time.
• Methodology: Mix of classroom and online content with extended discussions and assignments, literature review and paper implementation, capstone projects based on real-world problems and mentorship on soft-skills development.
• Duration: 10 Weeks.
• Factored will pay a monthly stipend to all program participants to cover living expenses during the accelerator program.
• Factored does not charge any upfront fees to enroll and participate in the program. However, participants will need to sign a loan agreement for an amount equal to the cost of their participation. This loan will be fully repaid and canceled after 2 years of full-time employment with Factored.
• After the program: We will conduct a technical assessment by the end of the accelerator program. Factored will extend full-time employment offers to participants who successfully complete and graduate from the accelerator program.

Admission Process:

• Online assessment: Applicants will receive an online assessment and will have 2 weeks to complete it. To prepare for the online assessment, you should: Refresh your Linear Algebra and Calculus knowledge, brush up on your Machine Learning and Deep Learning fundamental concepts. (bias and variance trade-off, common metrics, well-known models, well-known architectures, etc.) and practice basic Python (data structures and OOP) and Algorithmic Coding (e.g., solving LeetCode problems).
• Talent interview: Applicants who successfully pass the assessment will meet the recruitment team in an interview to measure communication skills and tech concepts.
• Tech interview: Applicants will discuss their experience, exploring their practical and conceptual understanding of the tools and skills used in past projects. Scenarios will be proposed, and applicants will explain how they would approach and solve them.
• Final interview: Applicants will have an interview with our People and Development Manager to determine alignment with the company's values, mission, and overall culture.

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.