ML Engineer

ML Engineer - Recommendation Systems

Hybrid - Parsippany, NJ (3 days/week onsite)

6+ months

Must haves:

• Recommendation Systems/ Ranking Systems
• Neural networks
• Deep Learning
• GRAPH
• DeepFM
• Matrix factorization

Interview Process:
Round#1: Introductory call to assess fit and communication style.

Round#2: Technical Interview (No coding test)

Round#3: Final in-person interview with Zaf, Vibhor and I.

Machine Learning Engineer – Recommendation Systems (Consumer Marketing)

We are seeking a skilled Machine Learning Engineer with deep expertise in building and optimizing recommendation systems within the consumer marketing space. The ideal candidate will have hands-on experience designing, implementing, and scaling personalized recommendation and targeting models that drive customer engagement, conversion, and revenue growth. Experience translating consumer behavior and marketing data into actionable, personalized experiences is essential.

Key Responsibilities:

• Design and develop machine learning models for recommendation and personalization systems (e.g., collaborative filtering, deep learning, hybrid approaches) tailored to consumer marketing use cases such as product recommendations, next-best-action, and audience targeting.
• Optimize models for scalability, performance, and real-time predictions across large-scale consumer datasets.
• Collaborate with business leaders, marketing partners, product and engineering teams to integrate models into production and campaign pipelines.
• Analyze and improve recommendation quality using metrics like precision, recall, click-through rate, conversion, and customer lifetime value.
• Leverage customer segmentation, behavioral, and first-party marketing data to enhance personalization and relevance.
• Experiment with cutting-edge techniques (e.g., reinforcement learning, graph neural networks, contextual bandits) to enhance recommendations and marketing outcomes.

Requirements:

• 5+ years of experience in machine learning, with a focus on recommendation systems, ideally within consumer marketing, retail, e-commerce, or a related consumer-facing domain.
• Proven track record building personalization or recommendation models that measurably improved engagement or marketing performance.
• Proficiency in Python, TensorFlow, PyTorch, or similar ML frameworks.
• Strong understanding of algorithms like matrix factorization, neural networks, and ranking systems.
• Strong understanding of LTMs and agentic AI frameworks that can be customized for recommender systems
• Experience working with consumer/marketing data, including behavioral, transactional, and campaign data (familiarity with CDPs, marketing analytics, or A/B testing is a plus).
• Experience with Databricks and AWS.
• Excellent problem-solving skills and a passion for delivering impactful, customer-centric solutions.