Machine Learning Engineer, Applied AI
Bjak · Remote · Portugal ·
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, starting with an agent-first mobile app.
You will own the development and deployment of ML models, focusing on practical problems like search relevance and personalization. The role requires balancing model quality with latency and cost in production environments.
What you'll do
- Build and improve ML models for classification, ranking, retrieval and personalization
- Optimize classifier accuracy, precision, latency and operational 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, latency, cost and reliability
- Strong ownership mindset and independent problem-solving abilities
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector search
- Ranking models
Hiring process
- Technical team evaluates initial applications
- Three to four interviews conducted virtually or onsite
- Prompt decision process valued for transparency and efficiency