About the role

This role will be operating at the intersection of applied research and engineering, building crucial AI capabilities, and owning their robust implementation. You will have direct impact on shaping the models served in our products, and the user experience as well as business value that is driven by them.

What you'll do

• Post-training, distillation and reinforcement learning
  • Create training environments that elevate the quality ceiling of synthetic data, and provide high-quality reward signals for both off- and on-policy learning.
  • Design model behaviors that optimize for interpretability and an outstanding user experience.
  • Train agentic models that autonomously interact with complex structured and unstructured environments, write and prototype code, and navigate multimodal inputs.
  • Utilize the best tool for the job – SFT over synthetic data, on-policy distillation, RL or any combination of these to drive down the cost of specialized intelligence.
  • Own benchmarking pipelines – assemble high-quality datasets and measure impact of different training parameters or methodologies.
• Engineering Principles
  • AI engineers are engineers – build production-ready code and own the end-to-end process of data, training, benchmarking and implementation.
  • Architect scalable inference strategies that maximize compute efficiency.
  • Maintain solutions to ensure stable, reliable execution for customers.
  • Closely collaborate with engineering team to design important intersections of AI and product services such as interfaces, inference tracking, checkpointing, data persistence and caching.
  • Contribute to engineering culture; mentor peers, evolve patterns, raise the bar on code quality, testing strategy, and documentation.
• Tooling & Resources
  • Work with bleeding-edge open-weights models and internal training frameworks.