Machine Learning Engineer, Ranking & Retrieval
Bjak · Remote · Poland ·
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 models for classification, ranking, and retrieval to enhance user communication and collaboration.
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 user 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, 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 constraints
- Strong ownership mindset and independent problem-solving capabilities
Stack
- Python
- PyTorch
- JAX
- Embeddings
- Vector Search
- Ranking Models
Hiring process
- Technical team evaluates applications initially
- Three to four interviews conducted virtually or onsite
- Prompt decision process valuing transparency and efficiency
- Offer extended upon demonstrating required skills and mindset