• 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.

• We are looking for someone who not only observes technical innovations but actively integrates them into our infrastructure. • You support the team responsible for infrastructure, DevOps and technical automation. • A particularly important part of your role is close collaboration with the Team Lead: you assist in preparing technical decisions, rapid prototyping and the rollout of new tools—especially AI-based solutions. • Develop our infrastructure further—you expand our deployment environments and automate where it noticeably improves stability, speed and transparency in everyday work. • Shape the DevOps culture—you take ownership of CI/CD, observability and security topics in the team’s day-to-day operations and ensure smooth processes. • Be a sparring partner for the Team Lead—you work closely with the Team Lead daily on architecture, workflows and current operational issues. • Pragmatically validate innovations—you use rapid prototyping to quickly test new ideas and technologies and assess their value for us. • Drive AI tools forward—you introduce new AI solutions, support onboarding and empower your colleagues through training and best practices.