Machine Learning Engineer (3x a week onsite in Morristown, NJ)

Machine Learning Engineer (hybrid onsite 3x a week in Morristown, NJ)
Long term contract position
Morristown, NJ
Rate = $85-90/hr (depending on if W2 or C2C)
Local candidates only (final interview is ONSITE)
 
Our Morristown, NJ Fintech client is seeking an experienced Machine Learning Engineer to build and productionize recommendation and personalization solutions for consumer-facing applications. The role will leverage behavioral, transactional, and engagement data to develop models that improve user experiences and support measurable business outcomes. Must have direct experience with recommendation systems and strong hands-on ML engineering skills.
 
Key Responsibilities

• Develop and deploy recommendation, ranking, targeting, and personalization models using techniques such as collaborative filtering, embeddings, neural networks, and learning-to-rank.

• Process large-scale datasets and support batch and real-time model inference.

• Partner with engineering, product, analytics, and business teams to integrate ML solutions into production applications and data pipelines.

• Establish model evaluation frameworks, conduct A/B testing, and optimize performance using technical metrics and business KPIs.

• Explore advanced approaches, including contextual bandits, reinforcement learning, LLMs, and agentic AI, for potential personalization applications.

 
Required Qualifications

• 5+ years of professional experience in machine learning, data science, or ML engineering, including meaningful recommendation or personalization experience, within e-commerce, retail, digital media, consumer technology, marketing technology, or another high-volume consumer environment.

• Experience with customer segmentation, audience targeting, marketing analytics, or customer data platforms.

• Proven experience building and operating production recommendation, ranking, targeting, or next-best-action models.

• Strong Python skills and experience with PyTorch, TensorFlow, or comparable ML frameworks.

• Strong understanding of recommendation algorithms, embeddings, ranking models, and large-scale behavioral or transactional data.

• Experience deploying ML workloads in cloud environments, with hands-on AWS and Databricks experience.

• Familiarity with modern LLM architectures and agentic AI frameworks.

• Previous experience building recommender systems within the Marketing and/or Retail industries.