Reka AI

• Build and ship AI-native product features end-to-end • Design and implement backend systems, APIs, and data pipelines that support model-driven workflows • Work across the stack when needed (backend, frontend, infra, experimentation) • Collaborate closely with researchers, designers, and product to bring ideas into production • Own systems from design → implementation → monitoring → iteration • Turn ambiguous goals into concrete scopes, milestones, and shipped outcomes • Move quickly while maintaining a high bar for reliability, performance, and simplicity

• Work with model researchers to define what “good data” means for our models, including quality metrics, validation checks, and acceptance thresholds • Explore open source datasets and create internal ones most suitable to build fundamental World Models • Build algorithms for automated data quality assessment, data domain mixtures, and domain adaptation from synthetic to real data. • Track datasets, metadata, provenance, and versions so experiments are reproducible and it’s clear what data went into which training and evaluation runs • Own CI/CD and development tooling for the data stack (GitHub, Python, PyTorch), and automate repetitive workflows to reduce friction • Track and optimize throughput, storage, and compute utilization across pipelines and related assets

• Translate cutting-edge research into production-ready machine learning systems • Design, build, and deploy end-to-end ML models and pipelines • Develop and optimize models for image and video processing • Own the full ML lifecycle: experimentation, training/fine-tuning, evaluation, and deployment • Rapidly prototype using open-source models and adapt them for product needs • Conduct experiments, analyze results, and iterate to improve performance • Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale • Participate with advancements in machine learning and apply them to continuously improve products

• Own the pipelines and storage systems for model training • Build tooling for automated and efficient processing • Optimize Python ETL pipelines for large-scale data • Maintain data cataloging, lineage, and quality tooling

• Design and implement improvements to training infrastructure • Directly contribute to technical decisions optimizing performance of models • Work on post-training processes, including reinforcement learning and fine-tuning • Improve the efficiency and scalability of model serving infrastructure

• Contribute to the entire development cycle of our cutting-edge large deep learning models. • Prepare datasets, design architectures, implement solutions, train and evaluate models to improve our products. • Collaborate closely with engineers and researchers to translate cutting-edge research into practical applications. • Join us at an exciting moment, wear many hats and help us build something from the ground up!

• Productionize frontier AI models to solve complex real-world problems. • Collaborate closely with researchers and other teammates on the latest advancements in AI and ML. • Work closely with our customers to integrate our models into their technology stack. • Make direct business impact with a high level of product ownership. • Be a founding member of a fast-growing team and wear many hats.