Machine Learning Engineer, Ranking & Retrieval
Bjak · Remote · Ireland ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- ML Ranking & Retrieval
- Python, PyTorch, JAX
- Production ML Systems
- Small High-Talent Team
Join a small, high-talent-density team building an agent-first mobile communication app designed to integrate AI into daily workflows with minimal prompting. You will own the development of ML systems for classification, ranking, and retrieval, taking projects from model creation to production deployment.
The team values independent execution and collective decision-making, aiming to eliminate misinformation and reduce coordination costs through intelligent, proactive applications.
What you'll do
- Build and improve ML models for classification, ranking, retrieval, and personalization.
- Optimize classifier efficiency, accuracy, precision, latency, and cost.
- Enhance search relevance using embeddings, hybrid search, and reranking techniques.
- Develop personalization models that learn from user behavior while preserving privacy.
- Create evaluation pipelines, labeled datasets, and metrics to track model quality.
- Fine-tune existing models when sufficient data is available.
- Maintain ML training, inference, and deployment pipelines.
- Debug production issues and monitor performance based on real-world feedback.
What you'll need
- Strong foundation in machine learning and modern neural architectures.
- Hands-on experience building and deploying ML models in production environments.
- Background in classification, ranking, retrieval, recommendation systems, or personalization.
- Understanding of model evaluation, experimentation, and performance metrics.
- Ability to write production-quality code and handle real-world datasets.
- Skill in balancing model quality, latency, cost, and reliability.
- Strong ownership mindset and problem-solving capabilities.
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector Search
- Ranking Models
Hiring process
- Technical team evaluates applications initially.
- Schedule 3 to 4 interviews if there is a fit.
- Interviews conducted virtually or onsite.
- Prompt decision provided after final assessment.