Senior Staff Machine Learning Engineer, ML Platform

What you'll do

• Design and develop model training and fine-tuning infrastructure at scale.
• Build a self-serve, unified platform for training, inference, and agentic AI.
• Develop high-performing machine learning systems from the ground up to support multiple internal teams.
• Implement distributed training techniques including pruning, compression, quantization, and distillation.
• Create tools and infrastructure specifically for model fine-tuning and large-scale deployment.
• Architect ML solutions that adhere to strict privacy commitments and data protection standards.
• Define and refine system architectures to solve unique challenges within the ad network.

What we're looking for

• Experience building shared ML platforms, frameworks, or services used by multiple teams or organizations.
• Deep understanding of the ML lifecycle, including training pipelines, evaluation methodologies, and deployment patterns.
• Deep understanding of deep learning architectures like Transformers, LLMs, and DNNs, and training frameworks such as TensorFlow and PyTorch.
• Prior experience applying ML at scale in ads, recommender systems, information retrieval, or related domains.
• Prior experience in distributed training at scale and optimization techniques like model pruning, compression, quantization, and distillation.
• Prior experience building AI/ML tooling for model fine-tuning and training, and infrastructure at scale.
• Ability to communicate effectively with both technical and non-technical multi-functional teams.
• PhD/MS/BS in Computer Science or related field with 10+ years of industry experience in building ML systems (preferred).
• Prior experience in privacy-preserving ML using techniques such as federated learning and differential privacy (preferred).
• Experience with LLM training, inference, Agentic AI, and SFT (preferred).