Machine Learning Engineer, Ranking & Retrieval

Bjak · Remote · Spain ·
Fully remoteFull-timeMiddle3–5 years
COMPENSATION
€•• – ••k
0 applicants so far

Listed on the employer's job board (Ashby)

  • Fully remote
  • Python + PyTorch/JAX
  • Ranking & Retrieval focus
  • Production ML systems

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 systems 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 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 provided after final interview stage