Sr. Machine Learning Engineer – Recommendation Systems

Job Title: Sr. Machine Learning Engineer – Recommendation Systems Location: Parsippany, NJ (Hybrid – 3 Days Onsite) Duration: Long-Term Contract Interview Mode: Face-to-Face (Mandatory) Experience: 14+ Years Job Summary STAFFXPERT LLC is seeking a Sr. Machine Learning Engineer – Recommendation Systems on behalf of our client in Parsippany, NJ. This role is ideal for a seasoned professional with a strong research background and extensive experience in building and scaling personalized recommendation systems. The candidate will play a critical role in designing, developing, and deploying advanced machine learning models that enhance user engagement and drive business outcomes. Key Responsibilities • Design, develop, and optimize recommendation systems using collaborative filtering, content-based, and hybrid approaches • Build and maintain scalable machine learning pipelines for both real-time and batch processing • Partner with cross-functional teams including Data Science, Engineering, and Product for end-to-end deployment • Evaluate model performance using metrics such as precision, recall, and user engagement • Implement advanced methodologies including reinforcement learning and graph neural networks • Ensure high performance, scalability, and reliability of ML solutions in production Required Skills and Experience • 5+ years of hands-on experience in machine learning with a strong focus on recommendation systems • PhD in Computer Science, Machine Learning, Data Science, or a related field (mandatory) • Strong programming expertise in Python • Proficiency with machine learning frameworks such as TensorFlow and PyTorch • Deep understanding of algorithms including matrix factorization, neural networks, clustering, and ranking systems • Experience working with cloud platforms and big data tools such as AWS and Databricks • Strong analytical, problem-solving, and communication skills Preferred Qualifications • Experience building large-scale, real-time recommendation engines • Strong research background with applied machine learning in production environments • Experience with A/B testing and experimentation frameworks