Machine Learning Engineer, Applied AI

Overview

Build and improve the ML systems that power Bjak's AI-native applications, owning models from development through production deployment.

What you'll do

• 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.

What you'll need

• 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.
• Tech stack: Python, PyTorch / JAX, embeddings, vector search and ranking models, production ML systems and inference pipelines.

Details

• Interviews will be conducted virtually and/or onsite.
• Interview process: 3, but no more than 4 interviews.
• Applications are evaluated by the technical team members.