Senior Machine Learning Engineer - Recommender Systems | $ 90K-$150K

Role Overview:

Operate at the intersection of classical ML, large-scale recommendation systems, and agentic AI , building and deploying intelligent systems for personalized experiences and next-gen autonomous AI workflows.

Key Responsibilities:

• Design and develop large-scale recommendation systems using ML, statistical modeling, ranking algorithms, and deep learning.
• Build, operate, and monitor ML models on high-volume, diverse datasets for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world impact.
• Build and experiment with agentic AI systems to autonomously improve workflows, tune hyperparameters, and optimize model performance.
• Apply LLMs, embeddings, RAG architectures, and multimodal generative models for semantic understanding and user preference modeling.
• Design intelligent agents to automate repetitive decision-making and feature selection.
• Collaborate with Product, Engineering, UX, and Business teams to integrate ML and AI features into user experiences.
• Contribute to thought leadership through internal tech talks, blogs, and industry events.

Required Skills & Experience:

• 6+ years in ML, recommendation systems, and personalization .
• Expertise in classical ML, deep learning, NLP, reinforcement learning, and time-series modeling .
• Hands-on experience with LLMs, RAG, generative models, and agentic AI workflows .
• Proficient in Python, PyTorch/TensorFlow, R, NumPy, SciPy .
• Experience with big data ecosystems (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
• Strong problem-solving, experimentation mindset, and cross-functional collaboration skills.

Preferred Qualifications:

• Masters or PhD in Computer Science, Statistics, Mathematics, Analytics, or related fields.
• Familiarity with privacy-preserving ML and identity-less ecosystems .
• Excellent communication skills to simplify complex technical concepts.

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