Unity Technologies
Technology
MachineLearningEngineer,Next-GenerationRecommendationSystems(NewGrad/PhD)
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“Machine Learning Engineer, Next-Generation Recommendation Systems (New Grad / PhD) at Unity Technologies. Skills: Data science, Machine learning, Data engineering”
Industry & Context.
What They're Looking For.
Must Have
SQL proficiency, Python proficiency
Nice to Have
PhD preferred, GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified certification, Dbt Certified certification, Experience with scikit-learn, Experience with TensorFlow, Experience with PyTorch, Experience with XGBoost, Experience with LightGBM, Experience with Hugging Face, Experience with MLflow, Experience with Snowflake, Experience with BigQuery, Experience with Redshift, Experience with Databricks, Experience with dbt, Experience with Fivetran, Experience with Airflow, Experience with dbt Cloud, Experience with Delta Lake, Experience with AWS SageMaker, Experience with GCP Vertex AI, Experience with Azure ML Studio
Full Job Description
The opportunity 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. What you'll be doing 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. What we're looking for 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.
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