Applied Machine Learning Engineer
Bjak · Remote · Sweden ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote
- Python + PyTorch/JAX
- ML Systems & Pipelines
- Small high-impact team
Join a mission to create proactive, AI-native applications for daily communication and workflows. You will build and improve ML systems that power an agent-first mobile app, minimizing the need for complex prompting.
The team values high talent density and independent execution. You will take ownership of model development from training to production deployment, solving practical problems to enhance user experience.
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 behavior while respecting privacy
- Create evaluation pipelines, labeled datasets and metrics for model quality
- Fine-tune existing models when supported by sufficient data availability
- Maintain ML training, inference and deployment pipelines for reliability
- Debug production issues and ship improvements based on real-world feedback
What you'll need
- Strong foundations in machine learning and modern neural architectures
- Hands-on experience building and deploying ML models in production
- Experience in classification, ranking, retrieval or recommendation systems
- Understanding of model evaluation, experimentation and performance metrics
- Comfortable writing production-quality code with real-world datasets
- Ability to balance model quality, latency, cost and system reliability
- Strong ownership mindset and problem-solving skills
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