Senior Machine Learning Engineer – Recommender Systems

This is a fully remote position, open to applicants in Latin America.

📋 Description

• Design and develop multi-stage recommendation systems, encompassing Candidate Generation (Retrieval), Ranking, and Re-ranking/Filtering phases.

• Create and refine machine learning and deep learning models to provide personalized recommendations using techniques such as Collaborative Filtering, Matrix Factorization, Two-Tower Networks, and advanced Deep Learning methods like Deep & Cross Networks.

• Construct and enhance two-stage retrieval architectures employing Approximate Nearest Neighbors vector search engines like Faiss, Pinecone, or Milvus.

• Implement real-time scoring and inference pipelines utilizing feature stores like Feast or Hopsworks, along with scalable serving frameworks such as Triton, TorchServe, or Ray Serve.

• Develop A/B testing frameworks, offline evaluation metrics including NDCG, MAP, and Recall@K, and real-time monitoring systems for tracking model performance and business metrics.

• Improve recommendation system latency and throughput via quantization, caching, and hardware acceleration techniques.

• Manage recommendation pipelines comprehensively from experimentation through to production deployment.

⛳️ Requirements

• Over 5 years of practical experience in machine learning and software engineering, with a track record of constructing and deploying large-scale recommender systems in a production environment.

• Proficient in English (both written and spoken) with excellent communication abilities to convey technical suggestions to cross-functional teams.

• Strong programming skills in Python, with expertise in PyTorch, TensorFlow, and recommendation libraries such as Surprise, LightFM, Implicit, RecBole, or NVIDIA Merlin.

• Proven experience with multi-stage recommendation methodologies, vector databases/ANN search technologies such as Faiss, Milvus, or Pinecone, feature stores, and contemporary serving frameworks.

• Solid understanding of AWS, GCP, Azure, MLOps pipelines, SQL, and Apache Spark for large-scale feature engineering.

🏝️ 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.