Applied Research - Evals & Data

Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment. Experience with applied data workflows and evaluation frameworks for large models or agents. Deep expertise in distributed training/inference frameworks. Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform). Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL. Passion for advancing the state-of-the-art in reasoning, measurement, and building practical, agentic AI systems.

Design and iterate on next-generation AI agents for workloads like automation and reasoning. Develop distributed systems, evaluation pipelines, and coordination frameworks. Build data capture, processing, and versioning workflows. Translate customer needs into technical requirements. Collaborate with RL and eval teams. Rapidly design and deploy agents, evals, and harnesses with customers. Prototype agents, data pipelines, and eval harnesses tailored to real use cases. Translate customer insights and evaluation results into roadmap and research direction. Design and implement novel RL and post-training methods. Build evaluation harnesses and verifiers. Integrate applied data collection and analytics into the post-training process. Prototype multi-agent and memory-augmented systems. Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making. Extend and integrate with agent frameworks. Architect and maintain distributed training and inference pipelines. Develop observability and monitoring for reliability and performance.