Requirements

B.S. or M.S. in Computer Science, Engineering, Applied Mathematics, or related field, or equivalent practical experience.
Proficiency in Python and common ML/DS libraries.
Experience with applied ML techniques such as information retrieval, embeddings, recommendation systems, or NLP.
Experience working with both structured and unstructured data (documents, text, logs, etc.).
Basic understanding of software engineering practices (e.g., testing, version control, CI/CD, containerization).

What you'll be doing

Explore and implement advanced RAG techniques and other applied ML methods to improve content relevance and answer quality.
Contribute to experiments in customer behavior and preference prediction, evaluating different approaches.
Build and maintain data pipelines to preprocess, enrich, and serve data for ML-driven features.
Fine-tune and integrate existing ML models into production systems (rather than building models from scratch).
Work with engineers and product managers to design ML-driven solutions aligned with product needs.
Contribute to monitoring, evaluation, and iteration on deployed ML features.

MLOps exposure (model deployment, monitoring, experiment tracking).
Experience with vector databases, search engines, or embedding-based retrieval.
Familiarity with distributed compute frameworks (Spark, Ray).
Knowledge of personalization, recommendation systems, or customer behavior modeling.
Exposure to REST APIs, Docker, Kubernetes.

Opportunity to work on applied AI scenarios that directly impact enterprise-scale products.
A collaborative environment focused on experimentation and innovation in search, personalization, and AI-driven user experiences.
Growth opportunities into advanced ML engineering and MLOps practices as our AI initiatives expand.