Senior Software Engineer (ML Focus) – Search & Recommender Systems

Senior Software Engineer (ML Focus) – Search & Recommender Systems

Location: Remote | Contract - US Based candidates only

Can only work with US Cit or GC Holders on this

We are seeking a Senior Software Engineer with a Machine Learning focus to lead the integration and operationalization of ML models in our Search area. This role requires close collaboration with data scientists and leadership teams, ensuring that ML models are seamlessly deployed and optimized for real-world applications. The ideal candidate has a strong foundation in MLOps methodologies and hands-on experience with Google Vertex AI and other cloud or open-source ML platforms.

Key Responsibilities:

• Develop and integrate machine learning models, particularly for recommender systems, into customer-facing products.
• Apply ML techniques such as embedding-based retrieval, reinforcement learning, and transformers to enhance search and recommendation functionalities.
• Optimize ML workflows by leveraging Vertex AI Feature Store to manage, share, and reuse ML features at scale.
• Implement feature stores as a central repository to maintain transparency and governance in ML operations.
• Design and deploy ML infrastructure, ensuring efficient use of CPU/GPU resources, networking, and security features for ML workloads.
• Conduct A/B testing and iterative optimization using data-driven approaches to enhance ML model performance.
• Ensure seamless data integration for AI applications, leveraging BigTable/BigQuery for executing ML models on business intelligence tools.
• Collaborate on data labeling and management, ensuring high-quality training data for ML models.

Qualifications:

• 5+ years of software engineering experience with a focus on ML model deployment and MLOps.
• Proven experience working with Google Vertex AI and other ML platforms.
• Hands-on expertise in recommender systems and related ML techniques.
• Understanding of A/B testing methodologies and iterative ML optimization.
• Experience with infrastructure scaling for ML workloads (CPU/GPU management, networking).
• Proficiency in BigQuery, BigTable, or similar data storage solutions for ML applications.
• Strong collaboration skills to work cross-functionally with data scientists, engineers, and leadership teams.

This is an exciting opportunity to work at the intersection of MLOps, search, and recommender systems, contributing to scalable and efficient ML-driven solutions.

If you’re passionate about building ML systems that enhance search experiences, we’d love to hear from you!