Member of Technical Staff - AI Data

Warp • South San Francisco, CA

1d

About The Position

Warp Sciences is building scientific infrastructure and autonomous laboratories that combine AI, robotics, automation, and life sciences to accelerate the discovery of new medicines. The company does not have a proprietary drug pipeline; it builds infrastructure that enables research teams to discover medicines faster. High-quality real-world data powers Warp's systems, models, and robots.

Warp is seeking a senior, hands-on AI data builder to own the data strategy and systems across Mol, its chemistry foundation model; XRay, its lab perception stack; and Bot, its robot-learning system. The role determines what data matters, where to source or generate it, and how to turn it into measurable capability gains. Data sources include open datasets, synthetic data, RL rollouts, expert feedback, teleoperation, and physical lab execution. The position connects these sources through Vault, Warp's shared data storage and lineage system, so data can be produced faster, trusted, and reused as the company scales.

Requirements

• Track record of improving model capabilities through better data, with hands-on ownership from identifying a gap to building a dataset and measuring results.
• Practical experience with ML data development, such as synthetic generation, model rollouts, multimodal datasets, expert annotation, or real-world collection.
• Strong understanding of data composition, sampling, demonstrations, rewards, and verifiers, and how they affect fine-tuning and reinforcement learning.
• Strong software engineering skills in Python, with experience building data pipelines, model-driven tools, and reproducible experiments.
• Good judgment about data quality, provenance, licensing, leakage, and evaluation, and ability to inspect and critically analyze examples.
• Ability to set priorities across teams while remaining hands-on, use AI tools and agents to multiply output and verify work, and collaborate with researchers, chemists, and robotics engineers.

Nice To Haves

• Experience with chemistry or scientific data, vision-language models, video understanding, or embodied AI.
• Background in teleoperation, demonstrations, simulation, active learning, or human-feedback workflows.
• Experience with RL-based data generation and selection, reward modeling, verifiable tasks, or large-scale synthetic-data pipelines.

Responsibilities

• Translate model failures and lab priorities into a data collection and generation plan, deciding the mix of open, licensed, synthetic, and real-world data and prioritizing coverage gaps, difficult cases, and expert involvement.
• Source and assess chemistry, scientific, and multimodal datasets; evaluate licensing, provenance, quality, and relevance; and build pipelines to extract, normalize, and enrich promising sources.
• Generate examples, model rollouts, and simulated trajectories; develop rewards, verifiers, and data filtering with researchers; use evaluation and RL feedback to improve generators, sampling, and curricula; and test for reward hacking, contamination, and transferability.
• Help design and conduct lab and teleoperation data collection with chemists and robotics teams, capturing demonstrations, corrections, failures, and recovery attempts in their experimental context.
• Build AI-native curation workflows using models and agents to label, enrich, deduplicate, and mine data, with human review where valuable; own quality checks, dataset composition, versioning, annotation, and feedback efficiency.
• Partner with researchers on data ablation, supervised fine-tuning, and RL experiments; use held-out evaluations and real failures to improve the data mix; and track capability lift, coverage, efficiency, and cost per useful example.
• Own the path from data collection and generation through ingestion, storage, lineage, retrieval, and dataset delivery, building clean interfaces and partnering with infrastructure engineers to keep the system fast, dependable, and maintainable.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed