Machine Learning Engineer

Summary

Join our Ad Tech team as a Machine Learning Engineer to help shape the future of digital advertising. This role involves applying groundbreaking research to large-scale systems, enhancing advertiser performance and user experiences, and impacting billions of ad impressions. You will design, build, and deploy machine learning models, develop solutions for batch and real-time systems, and collaborate with multi-functional teams. The position requires translating cutting-edge research into practical, scalable solutions and analyzing model performance. Relocation support is not available. The role requires strong analytical abilities and effective communication skills.

Requirements

• Background in machine learning, optimization, statistics, or reinforcement learning
• Proficiency in Python or similar programming languages, and familiarity with ML frameworks like TensorFlow, PyTorch, or JAX
• Solid software engineering skills and experience in production-grade systems
• Strong analytical abilities with experience in experimentation and making data-driven decisions
• Effective written/verbal communication to engage technical and non-technical audiences

Responsibilities

• Design, build, and deploy machine learning models for ad ranking, targeting, bidding, and attribution
• Develop solutions for both batch and real-time systems to positively influence important metrics like click-through rate, return on investment, revenue, and user engagement
• Collaborate with multi-functional teams including product managers, applied scientists, and backend engineers
• Translate cutting-edge research into production-ready solutions using scalable infrastructure and implementing best practices
• Analyze model performance, conduct A/B testing, and refine approaches based on the results

Preferred Qualifications

• Experience in advertising systems, recommendation engines, or large-scale prediction systems
• Familiarity with ad auctions, user modeling, causal inference, or performance optimization strategies
• Experience with large-scale data processing tools such as Spark, Kafka, or Airflow