Research Scientist Intern, Ai/ml, Core Ads Growth (phd)

Meta Meta · Big Tech · Zurich, Switzerland +1

Meta is seeking a PhD intern to work on machine learning systems and models for their Core Ads Growth team. The intern will develop scalable classifiers and tools, adapt ML methods for parallel environments, and contribute to building ML systems for Meta's products. The role involves research and development in areas like deep learning, NLP, recommendation systems, and computer vision.

What you'd actually do

  1. Develop highly scalable classifiers and tools leveraging machine learning, regression, and rules-based models.
  2. Suggest, collect and synthesize requirements and create effective feature roadmaps.
  3. Code deliverables in tandem with the engineering team.
  4. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

Skills

Required

  • Python
  • C++
  • Machine Learning
  • Deep Learning
  • NLP
  • Recommendation Systems
  • Computer Vision
  • Large-scale data analysis

Nice to have

  • Reinforcement Learning
  • Pattern Recognition
  • Signal Processing
  • Data Mining
  • Artificial Intelligence
  • Systems software
  • Algorithms
  • Distributed computing
  • GPU programming

What the JD emphasized

  • PhD in Computer Science, Computer Vision, Machine Learning, or related field
  • Research and/or work experience in a relevant field, such as machine learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision
  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or open-source code contributions
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading conferences or journals

Other signals

  • develop highly scalable classifiers and tools leveraging machine learning
  • adapt standard machine learning methods to best exploit modern parallel environments
  • build machine learning systems and models behind Meta’s products