Machine Learning Engineer (Next-Generation Recommendation Systems)

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Unity's Vector AI team builds the machine learning systems that decide which ads reach which players across billions of monthly users. This role focuses on designing and evaluating next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning. You will work across the full pipeline from training data to deployed models, partnering with engineering to bring research ideas into production.

• Design, build, and evaluate next-generation ranking and recommendation models incorporating LLMs, RLHF, and preference learning
• Develop user understanding systems such as conversion prediction, behavioral modeling, and value estimation
• Apply reinforcement learning and optimization to bidding strategy, auction dynamics, and real-time ad delivery
• Design and run experiments using causal inference, A/B testing, and offline evaluation frameworks
• Partner with engineering to bring research ideas into production across the full ML pipeline
• Communicate findings clearly to technical and non-technical stakeholders

• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate)
• 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 through research or industry internships
• Fluency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow
• A track record of rigorous, high-quality research, ideally with publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar)
• Strong written and verbal communication skills

• Industry experience in ads, recommendation, or user understanding systems
• Hands-on experience with production ML pipelines, training at scale, feature engineering, or experimentation infrastructure
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction
• Familiarity with agentic AI approaches such as multi-step reasoning, tool use, or human-AI collaboration frameworks
• Exposure to causal inference, uplift modeling, or A/B testing at scale