Senior Machine Learning Engineer (Recommender Systems)
💰 $150,000 – $220,000/yrMarket estimate · not provided by the employer
At a glance
- Who can apply
- Mexico, Brazil, Argentina, Chile, Colombia, Peru +2 more
- Work arrangement
- Remote · Latin America
- Employment type
- Full-time
- Seniority
- Senior
- Experience
- 5+ years (as stated by the employer)
- Salary
- $150,000 – $220,000/yr · market estimate
Checked against the employer's source on October 19, 2026. Details come from the employer's posting; confirm them on the application page.
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Job Description
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.