Recommender System Engineer, Ai-driven (pico-lab) - San Jose

ByteDance ByteDance · Big Tech · San Jose, CA · R&D

Recommender System Engineer focused on building and productionizing recommendation models, designing low-latency serving pipelines, and running experiments for XR products.

What you'd actually do

  1. Build and productionize recommendation models (collaborative filtering, content-based, hybrid, deep learning approaches).
  2. Design ranking and retrieval pipelines for low-latency, high-scale serving.
  3. Partner with product, data science, and engineering to define personalization strategy.
  4. Run offline/online experiments (A/B tests) and iterate using metrics such as CTR, retention, and revenue lift.
  5. Improve data pipelines, feature stores, and model monitoring for reliability and model quality.

Skills

Required

  • machine learning engineering
  • recommendation systems
  • ranking
  • Python
  • ML frameworks (PyTorch or TensorFlow)
  • large-scale data tools (Spark, Kafka, Airflow, SQL)
  • deploying ML models to production
  • relevance/ranking metrics
  • experimentation methodology
  • communication
  • cross-functional collaboration

Nice to have

  • PhD in Computer Science
  • Mobile Development
  • real-time personalization
  • feature stores

What the JD emphasized

  • productionize recommendation models
  • ranking
  • retrieval pipelines
  • low-latency
  • high-scale serving
  • deploy ML models to production
  • relevance/ranking metrics
  • experimentation methodology

Other signals

  • productionize recommendation models
  • design ranking and retrieval pipelines
  • run offline/online experiments
  • deploy ML models to production