• Design and build evaluation systems that assess educational AI quality across thousands of conversations - from learning outcomes to bias detection to curriculum alignment • Build ML systems that enable self-improving app creation - learning from high-quality apps on the platform to automatically scaffold new applications for educators • Design and prototype downloadable, on-device AI models that work without internet connectivity - critical for privacy and global accessibility • Develop systems that enable dynamic, fluid interfaces adapting to learning moments - transitioning seamlessly from chat to writing editor to interactive physics simulation as needed • Build content moderation and safety systems designed specifically for educational discourse • Implement agentic AI systems that enable educators to create goal-directed applications (e.g., "help students through this project over 2 weeks") • Build sophisticated RAG systems that integrate diverse educational content with semantic search and knowledge graphs

• Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. • We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. • Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs. • Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. • Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines. • Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

• Ship and support AI-native products from concept to production. • Apply sound judgment on building and maintaining high-performing production ML systems. • Stay deeply informed on the latest machine learning research and developments. • Collaborate with a team of strong engineers; serve as a mentor and resource for other high-potential ML engineers.

• Implement and integrate agent-based systems into operational workflows. • Build, deploy, and monitor ML/AI models in production (batch). • Design, build, and maintain large-scale geospatial data pipelines. • Develop backend services and ML tooling • Establish observability for pipelines, models, and agents (metrics, tracing, alerting). • Collaborate with product and customer teams to drive revenue.