Staff Machine Learning Engineer, Ads ML Efficiency

What you’d do

Design and build systems that improve the efficiency of ML training and inference workloads, including tooling for profiling, debugging, and monitoring model performance. Improve GPU and resource utilization through scheduling, caching, and workload optimization, and set technical strategy for ML platform scalability and cost efficiency. Remote in the United States. Found on the employer's careers page on Oct 14, 2026. Apply through the link.

You’ll need

• Experience with large-scale recommendation, ranking, generative AI, or foundation model systems
• Experience with distributed training frameworks such as PyTorch Distributed, Ray, TensorFlow, Spark
• Familiarity with GPU architectures and performance analysis tools
• Experience optimizing cloud infrastructure costs across large ML workloads