Factored

Senior Machine Learning Engineer (Recommender Systems)

Posted 23 Days Ago

11 Locations

Remote

Senior level

Artificial Intelligence • Machine Learning • Analytics

The Role

Design, deploy, and optimize large-scale recommender systems and machine learning pipelines. Build recommendation models using architectures such as Wide & Deep, Two-Tower, Transformer, embedding-based, autoencoder, and sequential models. Process data with Databricks and Spark, integrate models into production, monitor performance, conduct experimentation and A/B testing, and collaborate with engineering, data science, and business teams.

Summary Generated by Built In

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.

As a Machine Learning Engineer specializing in Recommender Systems, you will design, build, and optimize large-scale recommendation architectures that power personalized user experiences for enterprise environments. You’ll work across candidate generation, deep learning ranking models, and high-throughput real-time systems, owning recommendation pipelines end-to-end from experimentation to production deployment.

Functional Responsibilities:

Design and implement multi-stage recommendation pipelines, including Candidate Generation (Retrieval), Ranking, and Re-ranking/Filtering stages.
Develop and fine-tune machine learning and deep learning models for personalized recommendations using techniques such as Collaborative Filtering, Matrix Factorization, Two-Tower Networks, and Deep Learning (e.g., Deep & Cross Networks).
Build and optimize two-stage retrieval architectures using Approximate Nearest Neighbors (ANN) vector search engines (e.g., Faiss, Pinecone, Milvus).
Implement real-time scoring and inference pipelines using feature stores (e.g., Feast, Hopsworks) and scalable serving frameworks (e.g., Triton, TorchServe, Ray Serve).
Establish A/B testing frameworks, offline evaluation metrics (NDCG, MAP, Recall@K), and real-time monitoring for model performance and business metrics.
Optimize recommendation system latency and throughput using quantization, caching, and hardware acceleration.

Qualifications:

5+ years of hands-on experience in machine learning and software engineering, with proven experience building and deploying large-scale recommender systems in production.
Advanced English proficiency (written and spoken) with strong communication skills to articulate technical recommendations to cross-functional stakeholders.
Strong Python programming skills with expertise in machine learning frameworks (PyTorch, TensorFlow) and recommendation libraries (e.g., Surprise, LightFM, Implicit, RecBole, NVIDIA Merlin).
Demonstrated experience with multi-stage recommendation techniques, vector databases/ANN search (Faiss, Milvus, Pinecone), feature stores, and modern serving frameworks.
Solid background in cloud platforms (AWS, GCP, Azure), MLOps pipelines, SQL, and big data processing tools (Apache Spark) for feature engineering at scale.

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.

Skills Required

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field
5+ years of experience as a Machine Learning Engineer
At least 1 year of hands-on experience designing, building, and deploying recommender systems
Strong programming skills in Python
Experience with TensorFlow, PyTorch, or scikit-learn
Understanding and application of recommendation-system techniques, including Wide & Deep, Two-Tower, Transformers, embeddings, neural networks, autoencoders, and GRU4Rec
Extensive experience with large-scale data processing using Spark or PySpark within Databricks
Understanding of machine learning algorithms, deep learning, and statistical modeling
Knowledge of experimental design, A/B testing, and machine learning performance metrics
Experience with cloud platforms such as AWS, Azure, or GCP
Experience with Docker and containerization
Excellent verbal and written communication skills in English

View all jobs at Factored

View Factored Profile

Report Job

Am I A Good Fit?

beta

Get Personalized Job Insights.

Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.

The Company

HQ: Mountain View, CA

166 Employees

Year Founded: 2019

What We Do

Factored (backed by Andrew Ng's AI Fund and deeplearning.ai) helps leading tech companies select, upskill, and build world-class data science, machine learning and AI engineering teams much faster and more cost effectively. Our engineers have been personally vetted, educated, and mentored by some of the most talented and recognized AI educators and engineers from Silicon Valley, Stanford University and deeplearning.ai.