• Design and build systems that improve the efficiency of ML training and inference workloads. • Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance. • Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization. • Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. • Build benchmarking frameworks and performance dashboards for training and serving systems. • Optimize distributed training infrastructure, data pipelines, and model serving architectures. • Lead cross-functional initiatives that improve the productivity of Reddit ML engineers. • Drive technical strategy for ML platform scalability, reliability, and cost efficiency.

• Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models. • Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings. • Building data processing and inference pipelines for the models we develop. • Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments. • Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management. • Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance. • Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.

• Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models. • Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings. • Building data processing and inference pipelines for the models we develop. • Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments. • Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management. • Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance. • Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.

• Design and build systems that improve the efficiency of ML training and inference workloads. • Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance. • Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization. • Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. • Build benchmarking frameworks and performance dashboards for training and serving systems. • Optimize distributed training infrastructure, data pipelines, and model serving architectures. • Lead cross-functional initiatives that improve the productivity of Reddit ML engineers. • Drive technical strategy for ML platform scalability, reliability, and cost efficiency.