Senior Machine Learning Engineer – Recommender Systems
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Job Description
Senior Machine Learning Engineer designing large-scale recommender systems for Factored, which helps U.S. companies build AI, ML, and data teams with LATAM talent. Owning pipelines from experimentation to production.
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 Collaborative Filtering, Matrix Factorization, Two-Tower Networks, and Deep Learning such as Deep & Cross Networks
- Build and optimize two-stage retrieval architectures using Approximate Nearest Neighbors vector search engines such as Faiss, Pinecone, or Milvus
- Implement real-time scoring and inference pipelines using feature stores such as Feast or Hopsworks and scalable serving frameworks such as Triton, TorchServe, or Ray Serve
- Establish A/B testing frameworks, offline evaluation metrics including NDCG, MAP, and Recall@K, and real-time monitoring for model performance and business metrics
- Optimize recommendation system latency and throughput using quantization, caching, and hardware acceleration
- Own recommendation pipelines end-to-end from experimentation to production deployment
Requirements:
- 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 PyTorch, TensorFlow, and recommendation libraries such as Surprise, LightFM, Implicit, RecBole, or NVIDIA Merlin
- Demonstrated experience with multi-stage recommendation techniques, vector databases/ANN search such as Faiss, Milvus, or Pinecone, feature stores, and modern serving frameworks
- Solid background in AWS, GCP, Azure, MLOps pipelines, SQL, and Apache Spark for feature engineering at scale
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
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