주요업무
[Role]
- Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
- Build and evolve training and fine-tuning pipelines for large models.
- Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
- Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
- Build data pipelines and systems for high-quality real-world and synthetic training data.
- Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
- Partner closely with research and application engineering to turn model capabilities into product improvements.
- Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.
[Outcome]
- Research and models reliably translate into production-ready solutions with clear performance and quality targets.
- ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
- Production issues are detected, debugged, and resolved quickly, minimizing user impact.
- Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
- Iterations on models and systems are measurable, safe, and improve user experience over time.