Senior Recommender Systems Scientist

Amtex Systems Inc; Parsippany-Troy Hills (NJ); On-site; USD 150,000 - 190,000; Full time

Job summary

Amtex Systems Inc. in Parsippany‑Troy Hills, NJ, seeks a senior ML engineer to design and optimize recommendation systems for consumer marketing. You will build personalization models, scale them for real‑time predictions, and drive engagement and revenue growth.

You will collaborate with marketing, product, and engineering teams to productionize models, analyze performance with CTR, conversion, and CLV metrics, and explore advanced techniques like RL and graph neural networks.

Qualifications

• 5+ years of experience in machine learning with a focus on recommendation systems.
• Proven track record delivering personalized recommendations that improve engagement.
• Experience with consumer marketing data (behavioral, transactional, campaign).

Responsibilities

• Design and develop ML models for recommendation and personalization systems tailored to consumer marketing use cases.
• Optimize models for scalability, performance, and real-time predictions on large datasets.
• Collaborate with marketing, product, and engineering to productionize models in campaigns.
• Analyze and improve recommendation quality using relevant metrics.
• Leverage customer segmentation and first‑party data to enhance personalization.
• Experiment with RL, graph neural networks, and contextual bandits to improve outcomes.

Skills

Recommendation systems

Machine learning

Python

TensorFlow

PyTorch

Collaborative filtering

Deep learning

Rankings

Reinforcement learning

Graph neural networks

Tools

Databricks

AWS

CDPs

Job description

Amtex Systems Inc. in Parsippany‑Troy Hills, NJ, seeks a senior ML engineer to design and optimize recommendation systems for consumer marketing. You will build personalization models, scale them for real‑time predictions, and drive engagement and revenue growth.

You will collaborate with marketing, product, and engineering teams to productionize models, analyze performance with CTR, conversion, and CLV metrics, and explore advanced techniques like RL and graph neural networks.