Machine Learning Engineer - Recommendation Systems | $ 60K-70K

Role Overview:

Seeking a Data Scientist to operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI . Build and deploy intelligent systems for personalized user experiences and next-gen autonomous AI workflows.

Key Responsibilities:

• Design, develop, and maintain large-scale recommendation systems using ML, deep learning, ranking algorithms, and statistical modeling.
• Operate ML models on high-volume, diverse datasets for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure impact .
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering teams.
• Monitor model performance and identify drifts, failure modes, and optimization opportunities .
• Build and experiment with agentic AI systems that autonomously tune policies, orchestrate ML workflows, and improve system performance.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding and user preference modeling.
• Explore reinforcement learning, contextual bandits, and self-improving systems for next-gen personalization.

Required Skills & Experience:

• 2.55.5 years of experience in ML, recommendation systems, and AI-driven solutions .
• Strong programming skills in Python, PyTorch, TensorFlow, or equivalent .
• Hands-on experience with LLMs, embeddings, RAG pipelines, and agentic AI frameworks .
• Strong foundation in statistics, data modeling, and experimental design .

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