About the Role
Design and deploy machine learning models to improve search ranking and query understanding. Work closely with data scientists and engineers to scale systems and deliver measurable impact.
Responsibilities
- Develop machine learning models for search ranking and personalization
- Analyze large-scale user behavior data to identify patterns
- Optimize query interpretation and result relevance
- Collaborate with data engineering to build scalable pipelines
- Evaluate model performance using A/B testing frameworks
- Improve indexing strategies for faster retrieval
- Integrate natural language processing techniques into search
- Monitor system performance and troubleshoot issues
- Work with product teams to define success metrics
- Implement models in production with high reliability
- Research emerging techniques in information retrieval
- Contribute to model versioning and deployment workflows
- Ensure low-latency responses in high-traffic environments
- Maintain documentation for models and systems
- Participate in code reviews and system design discussions
- Support experimentation with new ranking features
- Refine feature engineering for model inputs
- Balance precision and recall in search results
- Use embeddings and semantic matching methods
- Collaborate on data labeling and annotation efforts
- Ensure models comply with privacy standards
- Scale infrastructure for global user base
- Iterate based on user feedback and analytics
- Drive improvements in click-through and conversion rates
- Stay current with academic and industry advancements
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid remote with team collaboration days
Team
Cross-functional team focused on search relevance and discovery
Tech Stack
Python, TensorFlow, PyTorch, Elasticsearch, Kafka, Airflow, Docker, Kubernetes, BigQuery, GCP
Impact
- Models influence search results for millions of users daily
- Direct contribution to core product discovery experience
Available for qualified candidates
