The opportunity
Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping…
What you'll do
Build, deploy, and operate production classification and evaluation systems: that provide useful feedback to post-training and research teams.
Optimize production inference, including model serving, batching, caching,: throughput, latency, compute utilization, and cost.
Train and optimize classifiers using supervised fine-tuning and distillation,: validating quality and efficiency before deployment.
Own the reliability and observability of deployed classifiers, diagnose: production issues, and improve their performance over time.
Design rigorous evaluations that assess accuracy, calibration,: generalization, and potential measurement bias while guiding production tradeoffs.
Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
What they're looking for
- Build reusable engineering tools that make classifier training, evaluation,: deployment, and iteration faster and more repeatable.
- Work with post-training researchers and data scientists to translate their: needs into modeling objectives, evaluation criteria, and production requirements.
- Experience taking ML projects from problem formulation and experimentation through production deployment.
- Strong foundations in supervised learning, statistics, and experimental design.