Machine Learning Engineer, Applied AI
Bjak · Remote · Seoul, Korea ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote
- Python + PyTorch/JAX
- Production ML focus
- Small high-talent team
Join a team building proactive, AI-native applications for daily communication and coordination. The goal is to integrate intelligence into workflows with minimal prompting, eliminating misinformation and reducing coordination costs.
You will own the lifecycle of ML systems from development to production deployment. The work involves solving practical problems in model quality, reliability and user-facing performance for a global audience.
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 model 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 independent problem-solving abilities
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector search
- Ranking models
How you'll work
- Remote work environment
- Interviews conducted virtually or onsite
- Small, hands-on team structure
- Collective decision-making process
Hiring process
- Technical team evaluates initial applications
- Three to four interviews scheduled if fit is identified
- Virtual or onsite interview formats available
- Prompt decision provided after final interview stage