Machine Learning Engineer, Applied AI
Bjak · Remote · Singapore ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote potential
- Python + PyTorch/JAX
- Production ML focus
- AI-native mobile app
- Small high-talent team
Join a team building an agent-first mobile communication app designed to integrate AI into daily tasks like email and calendar management. The goal is to enable high-quality collaboration and organization without requiring complex user prompting.
You will own the lifecycle of ML models, handling classification, retrieval, ranking, and personalization. The work emphasizes shipping reliable systems that improve model quality and serve real users effectively.
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 privacy-respecting personalization models based on user behavior
- Create evaluation pipelines, labeled datasets and metrics for model quality
- Fine-tune existing models when sufficient data is available
- Maintain ML training, inference and deployment pipelines
- Debug production issues and monitor performance using real-world feedback
What you'll need
- Strong foundation in machine learning and modern neural architectures
- Hands-on experience deploying ML models in production environments
- Background in classification, ranking, retrieval or recommendation systems
- 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 reliability
- Strong ownership mindset and 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 provided after final stage