Senior Machine Learning Engineer – Recommender Systems

Job not on LinkedIn

🔥 3 minutes ago

💃 Latin America – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Factor Funding Company

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 1996

📦 Logistics

🏭 Manufacturing

💼 Consulting

Logistics • Manufacturing • Consulting

Factor Funding Company is a financial services provider specializing in small business funding through various options such as accounts receivable factoring, asset-based loans, and purchase order financing. By converting invoices and receivables into immediate cash, they help businesses quickly improve cash flow without the lengthy processes associated with traditional lending. Their services cater to the needs of small businesses across multiple industries, allowing them to focus on growth and operations while alleviating cash flow concerns.

📋 Description

• 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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