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 lifecycle of ML systems from development to production, solving practical problems in model quality and reliability for real users.
What you'll do
- Build and improve ML models for classification, ranking, retrieval and personalization
- Optimize classifier accuracy, precision, latency and cost efficiency
- 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 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 based on 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 problem-solving capabilities
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector search
- Ranking models
How you'll work
- Remote work available
- Virtual and/or onsite interviews conducted
- Small, hands-on team structure
- Collective decision making process
Hiring process
- Technical team evaluates applications
- Three to four interviews maximum
- Interviews held virtually or onsite
- Prompt decision provided after final stage