Become a ML Recommender Systems Engineer

Latin America

Full-time

Job Description

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.

BOOSTING PROGRAM!

We are excited to launch a six-week Boosting Program, a full-time intensive experience designed to transform strong quantitative and coding skills into cutting-edge expertise. This program focuses on building advanced capabilities in areas such as recommender systems, the technology that powers the future of personalization.

As a Recommender Systems Specialist at Factored, you’ll work at the intersection of advanced ML models, large-scale data, and real-world personalization challenges. You’ll gain deep expertise in retrieval, refinement, ranking, and re-ranking pipelines while leveraging distributed computing and state-of-the-art deep learning approaches. Besides training models, you’ll design, deploy, and optimize end-to-end recommendation systems that balance accuracy, fairness, scalability, and business impact.

Functional Responsibilities:

Design and deploy recommender systems across the whole pipeline.
Build collaborative filtering, content-based, hybrid, and deep learning models (e.g., deep factorization machines, two-tower, sequence-based models).
Develop scalable ML pipelines for data ingestion, feature engineering, training, and deployment.
Leverage distributed systems (Spark, PySpark) to handle large datasets efficiently.
Deploy models via APIs and serving frameworks (FastAPI, Flask).
Evaluate models with ranking and personalization metrics: AUC, precision@k, recall@k, MAP, NDCG, diversity, serendipity.
Conduct A/B tests and experiments to improve production systems continuously.
Collaborate with business, marketing, and engineering teams to ensure alignment between recommendations and business goals.
Communicate results effectively to both technical and non-technical stakeholders.

Qualifications:

Master’s degree or higher in Computer Science, Data Science, Statistics, Mathematics, or a related field.
5+ years of professional experience as an ML Engineer, Data Scientist, or related role.
Experience in retail or e-commerce industries.
Strong programming in Python with ML libraries (TensorFlow, PyTorch, Scikit-learn).
Deep understanding of Deep Learning and different Neural Network architectures like Transformers.
Proficiency in feature engineering for user–item interactions, sparse/multi-list data, and embeddings.
Hands-on experience with Spark/PySpark for distributed data processing.
Proficiency in model deployment (APIs, serving frameworks).
Experience working with cloud platforms (e.g., AWS, Azure, GCP).
Excellent English communication skills.

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 are 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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