Machine Learning Engineer (Recommender Systems)
Fully remote | Complete engagement job
Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.
At Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.
We are seeking a Machine Learning Engineer who is passionate about building state-of-the-art recommender systems and leveraging Generative AI. You'll work with large-scale data using tools like Databricks and Spark, contributing to innovative AI solutions that enhance personalized experiences while being part of a supportive, dynamic, and collaborative team. In return, you will be rewarded with an amazing team that supports you, a rich culture, shared success, and the flexibility to work– from the comfort of your home.
Functional Responsibilities:
- Design and implement recommender systems to improve product discovery and enhance customer engagement across digital and physical platforms.
- Build and manage scalable machine learning pipelines for data processing, feature engineering, model training, and deployment using tools like Databricks and Spark.
- Apply and optimize advanced machine learning models for recommendation systems, including Wide & Deep models, Two-Tower architectures, Transformer-based models (e.g., NRMS), embeddings-based approaches, neural networks, autoencoder-based models (e.g., AutoRec), and deep sequential models like GRU4Rec.
- Collaborate closely with software engineers, data scientists, and business stakeholders to integrate models into production systems and solve real-world business challenges.
- Monitor, maintain, and continuously enhance deployed models to ensure reliability, accuracy, and alignment with evolving business needs.
- Stay informed on the latest advancements in machine learning, recommender systems, deep learning, and Generative AI to drive innovation and improvement.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
- 5+ years of proven experience as a Machine Learning Engineer, demonstrating successful development and deployment of Machine Learning models.
- Minimum 1 year of hands-on experience designing, building, and deploying recommender systems. This is a must-have requirement.
- Strong programming skills in languages such as Python along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.S
- olid understanding and application of machine learning techniques relevant to recommendation systems, including but not limited to Wide & Deep models, Two-Tower models, Transformers, embeddings, neural networks, autoencoders (AutoRec), and deep sequential models (GRU4Rec)
- Extensive experience handling large-scale data processing and analysis using Spark/PySpark within Databricks, including its native platform services.
- Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
- Strong knowledge of experimental design, A/B testing, and performance evaluation metrics for machine learning solutions.
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (Docker) is a plus.
- Excellent verbal and written communication skills in English.
Our Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance 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 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.