Member of Technical Staff, Data Flywheel

Role Overview

The Data Flywheel team closes the gap between benchmark performance and useful performance in the real world. We identify and build the signals, data, and feedback loops that turn model usage into rigorous evaluations, targeted training data, and measurable improvements in future generations of models.

This is a hands-on technical role at the intersection of research and deployment. You'll take ambiguous model behaviors from first observation through measurement, intervention, and validated improvement, working across evaluation, human and synthetic data, infrastructure, post-training, and live deployments. You'll collaborate closely with researchers and engineers across the company, as well as customers, partners, vendors, and the open-source community.

What You’ll Do

• Identify high-value data sources and partnership opportunities, deeply understand the underlying use cases, and translate them into representative evaluations

• Bring new data sources online, from initial partner conversations and data scoping through quality validation and integration into production evaluation and training pipelines, where appropriate

• Design and build evaluations, graders, and feedback loops that make priority real-world model behaviors measurable

• Analyze model performance and failure modes, then translate those insights into targeted datasets, reward signals, and training interventions

• Develop human and synthetic data strategies for capabilities where existing data is insufficient, including designing and running evaluation and data-collection programs with vendors

• Build the infrastructure and pipelines needed to ingest, inspect, version, and evaluate data reliably at scale

• Collaborate closely with pre-training, post-training, applied, and partnership teams to turn new signals into measurable model improvements

What We’re Looking For

• Degree (BS, MS, or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience

• Deep technical understanding of LLM training and evaluation, with hands-on experience in areas such as evaluation design, data curation, reinforcement learning, or reward design

• Strong software engineering skills and experience building automated data/evaluation pipelines or large-scale ML systems

• A track record of owning high-impact projects end to end, navigating ambiguity, and adapting quickly as priorities change

• A highly collaborative, action-oriented approach and excitement about defining how a new frontier lab measures and accelerates model progress

• High agency and thrive in a fast-paced startup environment; bias for impact over process

• Enjoy collaborating across research, engineering, operations, and product disciplines

• Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.