Unity Technologies-posted 2 days ago
Full-time • Senior
San Francisco, CA

At Unity, we’re committed to building a win-it-together culture grounded in respect and opportunity. Within our fast-paced and collaborative environment, we’re tackling complex challenges that drive meaningful impact for gamers and game developers across our ecosystem. The Unity Ads Demand Optimization team plays a critical role in this effort. We build systems and algorithms for auction and bidding to optimize on behalf of advertisers, with consideration of multiple factors (e.g. objective, budget, target ROAS and so on). We design optimized solutions for campaign products to improve advertisers' experience. We are seeking skilled MLEs to design and implement AI-native demand optimization algorithms and systems. You will own end-to-end solutions that power autobidding, auction mechanics, and reinforcement learning strategies for ads and games. This role is highly cross-functional and impact-driven, working closely with product, engineering, and scientists to deliver measurable improvements in delivery efficiency, revenue, and user experience.

  • Architect and implement automated bidding strategies to improve delivery efficiency
  • Design, prototype, and productionize new bidding algorithms and products, including model training, policy evaluation, and guardrail enforcement.
  • Develop and refine auction mechanisms that balance multiple objectives under constraints.
  • Build exploration frameworks and reinforcement learning strategies for cold-start scenarios (new ads/new games), including contextual bandits, off-policy evaluation, and online learning.
  • Own the full lifecycle: problem framing, data instrumentation, modeling, simulation, experimentation, and deployment with continuous monitoring and iteration.
  • Advanced degree (MS or Ph.D.) in Computer Science, Machine Learning, Statistics, or a related field—or equivalent practical experience.
  • Strong foundation in: Reinforcement learning, contextual bandits, and exploration-exploitation strategies.
  • Auction theory, mechanism design, and multi-objective optimization.
  • Probabilistic modeling, causal inference, and counterfactual evaluation.
  • Online learning, off-policy evaluation, simulation, and A/B testing.
  • Proficiency with Python and common ML libraries (e.g., PyTorch, TensorFlow, JAX); experience with data processing (e.g., Spark, SQL) and experiment platforms.
  • Demonstrated ability to translate business objectives into robust algorithmic solutions and measurable outcomes.
  • 3+ years of hands-on experience building and operating large-scale Ads delivery and optimization systems.
  • We offer a wide range of benefits designed to support employees' well-being and work-life balance. You can read more about them on our career page .
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