Machine Learning Engineer, Next-Generation Recommendation Systems (New Grad / PhD)

Unity Technologies•Mountain View, CA

8h•$112,700 - $169,100

About The Position

Unity's Vector AI team builds the machine learning systems that decide which ads reach which players — across billions of monthly users on the world's leading game engine. Recommendation and ranking systems are the core of this work: predicting user value, optimizing bids, and delivering outcomes for advertisers at massive scale. We are building the next generation of these systems. The frontier has shifted — large language models, reinforcement learning from human feedback, and agentic AI are reshaping what recommendation systems can do. We are looking for PhD graduates who have worked at that frontier and want to bring those ideas into production systems that matter.

Requirements

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.
Experience working with large-scale data and ML systems, whether through research or industry internships.
Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
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.
Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.

Nice To Haves

Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
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 problems.
Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
Exposure to causal inference, uplift modeling, or A/B testing at scale.
Genuine curiosity about applied research and the drive to see ideas through to impact.

Responsibilities

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