Build and improve ML systems for AI-native applications, including an agent-first mobile communication app. Develop and deploy models for classification, retrieval, ranking and personalization using Python, PyTorch and JAX.
Strong foundations in machine learning and modern neural architectures.
Hands-on experience building and deploying ML models in production.
Experience in classification, ranking, retrieval, recommendation systems or personalization.
Understanding of model evaluation, experimentation and performance metrics.
Comfortable writing production-quality code and working with real-world datasets.
Able to balance model quality, latency, cost and reliability.
Strong ownership and problem-solving skills.
Build and improve ML models for classification, ranking, retrieval and personalization.
Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
Build personalization models that learn from user behavior while respecting privacy.
Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
Fine-tune and adapt existing models when supported by sufficient data.
Build and maintain ML training, inference and deployment pipelines.
Debug production issues, monitor model performance and ship improvements based on real-world feedback.
If there appears to be a fit, the company will schedule 3, but no more than 4, interviews.
Applications are evaluated by technical team members; interviews are conducted virtually and/or onsite.
