Senior Machine Learning Engineer – Recommender Systems

Factor Funding Company

. Design and implement multi-stage recommendation pipelines, including Candidate Generation (Retrieval), Ranking, and Re-ranking/Filtering stages .

Posted 10/31/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and implementing multi-stage recommendation systems, with a strong focus on machine learning and deep learning techniques. Proficient in optimizing model performance and deploying scalable solutions in cloud environments.

Highest-signal resume keywords

Machine LearningDeep LearningRecommendation SystemsPython ProgrammingAWS

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Collaborative FilteringMatrix FactorizationTwo-Tower NetworksDeep & Cross NetworksA/B TestingNDCGMAPRecall@KQuantizationCaching

Soft Skills

Strong Communication SkillsCross-Functional Collaboration

Tools & Technologies

PyTorchTensorFlowFaissPineconeMilvusFeastHopsworksTritonTorchServeRay Serve

Industry Keywords

MLOpsSQLApache SparkFeature EngineeringCloud Platforms

Tech Stack

Tools & technologies

ApacheAWSAzureGoogle Cloud PlatformPythonPyTorchRaySparkSQLTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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