About the job
This job will drive the strategic vision and development of cutting-edge machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences.
Responsibilities
Define and drive the strategic vision for machine learning initiatives across multiple teams or projects.
Lead the development and optimization of state-of-the-art machine learning models.
Oversee the preprocessing and analysis of large datasets.
Deploy and maintain ML solutions in production environments.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models, making necessary adjustments.
Mentor and guide junior engineers and data scientists.
Publish research findings and contribute to industry discussions.
Lead the development of new methodologies and frameworks for ML applications.
Qualifications
Minimum
10+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
Deep expertise with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Extensive experience with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
Proven track record of leading the design, implementation, and deployment of machine learning models.
Preferred
10+ years in ML engineering, with deep expertise in recommendation systems, search ranking, or personalization at consumer scale
Production experience with learning-to-rank, contextual bandits, or real-time recommendation systems serving millions of users
Track record building ML that drives business metrics — you think in terms of engagement, conversion, and retention, not just model accuracy
Experience with social platform ML: feed ranking, social graph models, content discovery, or network growth — at a company where social interaction is core to the product
Strong platform design skills — feature stores, model serving, experiment infrastructure
Experience with graph-based ML: social graph embeddings, transaction graphs, or knowledge graphs
Strong data engineering instincts — BigQuery, Spark, Airflow, dbt — you understand the full pipeline from raw data to model prediction