About the role
Defining the ad relevance problem across different ad scenarios to optimize both the user and advertiser experience. Driving algorithmic and modeling improvements to the system using primarily deep learning techniques from NLP and computer vision, including the latest LLM models. Deploying robust and scalable solutions to continuously improve ad relevance. Analyzing model and system performance to identify opportunities based on offline and online testing. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 4+ years of experience developing natural language processing or multimodal machine learning systems using deep learning, including hands-on experience with transformer-based small language models (SLMs) or large language models (LLMs). OR 4+ years working experience in Computer Vision (CV) with latest deep learning technologies including Vision Transformers. 4+ years of experience developing and operating production machine learning or AI systems using Python, C++, or equivalent programming languages. Experience in online advertising. Experience with distributed training or inference for SLMs and LLMs, including data and model parallelism, mixed-precision training, checkpointing, experiment management, performance optimization, and efficient serving. Ability to work independently in a team to deliver innovative solutions solving challenging business/technical problems from high level vision and architecture, down to quality design and implementation. Experience evaluating SLMs, LLMs, or agentic systems using task-specific offline metrics, human or model-assisted evaluation, safety and robustness testing, latency and cost analysis, and controlled online experiments. Experience designing and implementing agentic AI systems that use tool calling, retrieval, planning, memory, structured outputs, multi-step workflows, or multi-agent coordination, with appropriate safeguards and observability. Experience applying responsible AI practices to model and agent development, including evaluation for safety, reliability, privacy, security, bias, groundedness, and misuse risks. Have publications at peer-reviewed Data Science/AI conferences (e.g. KDD- Knowledge Discovery and Data Mining, CIKM- Conference on Information and Knowledge Management, SIGIR- Special Interest Group on Information Retrieval, NeurIPS- Neural Information Processing Systems, CVPR- Computer Vision and Pattern Recognition, ICML International Conference on Machine Learning, ICLR- International Conference on Learning Representations, ICCV- International Conference on Computer Vision, and ACL- Association for Computational Linguistics).
