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# Senior Machine Learning Engineer (Recommender Systems)

- **Company:** Factored, Inc.
- **Location:** Palo Alto, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Big Data, Computer Programming, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Data Processing, Google Cloud, Feature Engineering, Pytorch, Apache Spark, Deep Learning, Generative AI, Containerization, Pyspark, Information Technology, Machine Learning Operations, GPT, Software Library, Docker, Databricks
- **Published:** September 18, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pl4ttrlsgv

## About the Role

We are seeking a Senior 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., * 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 amust-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.

## Description

* 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.


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