Machine Learning Engineer

Who you are

• 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
• 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

What the job involves

• 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
• 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

Benefits

• Comprehensive health, life and disability insurance
• Employee stock ownership
• Generous vacation and personal days
• Office food and free snacks, lots of health options!
• Employee Resource Groups
• Training and development programs
• Commute subsidy
• Competitive retirement/pension plans
• Paid leave for new parents
• Mental Health and Wellbeing programs and support
• Global Employee Assistance Program
• Volunteering and donation matching program