• Master Socket workflows, tools, and patching processes • Lead patching efforts for high-impact vulnerabilities across npm packages • Scale patch production to dozens or hundreds of patches per week • Help select and prioritize high-value patches • Provide technical input on patch prioritization based on ecosystem and customer impact • Build and improve automated patching infrastructure and tooling • Design and implement scalable patch generation and delivery systems • Develop automated vulnerability detection and patch creation workflows • Build APIs and integrations to deliver certified packages • Create tooling for patch quality assurance and testing • Work with security researchers to understand and patch critical vulnerabilities • Help shape the technical roadmap for expansion • Give developers quick, safe remediation options for widely-used packages • Help secure the software supply chain for millions of developers

• Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution • Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute & memory optimization techniques. • Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks. • Publish research in top-tier AI conferences such as ICML & NeurIPS. • Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers. • Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.

• Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads. • Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers. • Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements. • Work on distributed training systems spanning data, tensor, and pipeline parallel workloads. • Help shape the architecture of our RL training stack, including async rollout and post-training systems. • Contribute to open-source libraries and internal infrastructure used for frontier-scale model training. • Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements. • Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

• Creating and upholding a reliable and expandable data management system specialized for text-to-speech projects. This includes establishing guidelines for versioning and ensuring data quality. • Establishing a streamlined process for autonomously training, assessing, and launching text-to-speech models. This encompasses implementing procedures for dynamic learning, as well as routines for fine-tuning and refreshing validation data. • Investigating cutting-edge approaches and strategies in machine learning, deep learning, and algorithms pertaining to text-to-speech technology.