Machine Learning Engineer (Next-Generation Recommendation Systems)

Unity; Mountain View (CA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Stock ownership

Generous vacation

Office snacks

Employee Resource Groups

Training programs

Commute subsidy

Retirement plan

Parental leave

Mental health support

EAP

Volunteering match

Job summary

Unity is seeking PhD graduates to join the Vector AI team to design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.

You will develop user understanding systems—conversion prediction and behavioral modeling—operating across billions of impressions, and apply reinforcement learning to bidding strategy and real-time ad delivery, collaborating with engineering and product teams.

Qualifications

• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow
• Strong written and verbal communication skills
• PhD in Computer Science, Machine Learning, Statistics, or related field (graduating 2026 or recent graduate)
• Strong research foundations in one or more: 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
• A track record of rigorous research — top venue publications
• 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
• Familiarity with agentic AI approaches
• Exposure to causal inference, uplift modeling, or A/B testing

Responsibilities

• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning
• Develop user understanding systems — conversion prediction and behavioral modeling across billions of impressions
• Apply reinforcement learning 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
• Partner with engineering to bring research ideas into production across training data to deployed models
• Communicate findings clearly to technical and non-technical stakeholders across teams

Skills

Python

ML frameworks

Communication

PhD in CS/ML/Stats

Reinforcement learning

Recommendation systems

LLMs/RLHF

Large-scale data

Production ML pipelines

A/B testing / causal inference

Education

PhD in Computer Science, Machine Learning, Statistics, or related field

Job description

• 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

Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlowStrong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiencesPhD 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 optimizationExperience working with large-scale data and ML systems, whether through research or industry internshipsA track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signalIndustry 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 infrastructureExperience applying LLMs or generative models to ranking, retrieval, or structured prediction problemsFamiliarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworksGenuine curiosity about applied research and the drive to see ideas through to impactExposure to causal inference, uplift modeling, or A/B testing at scale