Machine Learning Engineer, Ranking & Retrieval
Bjak · Remote · Singapore ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- ML Ranking & Retrieval
- Python + PyTorch/JAX
- Production ML Systems
- AI-Native Mobile App
- Small High-Talent Team
Join a team building proactive, AI-native applications for email, notes, and tasks that require minimal prompting. As a Machine Learning Engineer, you will own the development and deployment of systems for classification, ranking, and retrieval, directly impacting user experience in a new agent-first mobile communication platform.
What you'll do
- Build and improve ML models for classification, ranking, retrieval, and personalization.
- Optimize model accuracy, precision, latency, and cost for efficient classifiers.
- Enhance search relevance using embeddings, hybrid search, and reranking techniques.
- Develop personalization models that learn from user behavior while ensuring privacy.
- Create evaluation pipelines, labeled datasets, and metrics to track model quality.
- Fine-tune existing models when sufficient data is available for adaptation.
- Maintain ML training, inference, and deployment pipelines for reliability.
- Debug production issues and ship improvements 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, 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 with latency, cost, and system reliability.
- Strong ownership mindset and independent problem-solving capabilities.
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector Search
- Ranking Models
Hiring process
- Technical team evaluates initial applications.
- Schedule three to four interviews maximum.
- Interviews conducted virtually or onsite.
- Prompt decision process with transparent feedback.