Machine Learning Engineer, Ranking & Retrieval

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

Listed on the employer's job board (Ashby)

  • ML Ranking & Retrieval
  • Python, PyTorch, JAX
  • Production ML Systems
  • AI-native Mobile App
  • Remote/Onsite Interviews

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 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, recommendation, or personalization.
  • 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 system reliability.
  • Strong ownership mindset and effective problem-solving capabilities.

Stack

  • Python
  • PyTorch
  • JAX
  • Embeddings
  • Vector Search
  • Ranking Models

Hiring process

  • Technical team evaluates applications for initial fit.
  • Schedule three to four interviews maximum if matched.
  • Interviews conducted virtually or onsite depending on location.
  • Prompt decision provided after demonstrating required skills.