Machine Learning Engineer, Next-generation Recommendation Systems (new Grad / Phd)

Unity Unity · Enterprise · New York, NY · Students & Early Career

This role focuses on designing, building, and evaluating next-generation recommendation and ranking systems for ads, incorporating LLMs, RLHF, and preference learning. It involves developing user understanding systems, applying reinforcement learning to bidding strategies, and conducting rigorous experiments. The role partners with engineering to bring research ideas into production across the full pipeline.

What you'd actually do

  1. Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
  2. Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
  3. Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
  4. Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
  5. Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.

Skills

Required

  • PhD in Computer Science, Machine Learning, Statistics, or a related field
  • Strong research foundations in recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization
  • Experience working with large-scale data and ML systems
  • Fluency in Python
  • Familiarity with ML frameworks such as PyTorch or TensorFlow
  • Track record of rigorous, high-quality research (publications at top venues)

Nice to have

  • Industry experience in ads, recommendation, or user understanding systems
  • Hands-on experience with production ML pipelines
  • Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems
  • Familiarity with agentic AI approaches
  • Exposure to causal inference, uplift modeling, or A/B testing at scale
  • Genuine curiosity about applied research

What the JD emphasized

  • PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate)
  • Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
  • A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.

Other signals

  • LLMs
  • RLHF
  • recommendation systems
  • ranking systems
  • large-scale data
  • production systems