Overview Build the automation and data tooling our synthetic chemists rely on to develop and validate reactions, and own quality controls in our data generation pipeline. On the build side, translate an experimental design into actionable automation: plate maps, worklists, and instrument parameters generated rather than assembled by hand. On the quality side, define the controls and acceptance criteria that keep execution consistent plate to plate and month to month, and pursue experiments that identify where quality drifts so we can continuously raise the bar.

You will own: - The automation tooling used by the synthetic chemistry team: build it, refine it as they use it, and close the gap between what chemists want to run and what the instruments can execute. - What the data-generation step requires: specify what a condition set must prove before running at scale—plate design, controls, replication, and validation criteria. The chemist owns the reaction; you own whether the experiment is built so results are trustworthy in production. - Process QC in the data generation pipeline: the controls, anchors, replication, and plate-acceptance criteria that catch a bad run while it is still a bad run. - Execution quality once campaigns are running: whether a plate was run to standard, consistency across operators and decks and months, and what happens when a run fails partway through. - Finding where the process loses quality and fixing it: side experi

Overview Build the automation and data tooling our synthetic chemists rely on to develop and validate reactions, and own quality controls in our data generation pipeline. On the build side, translate an experimental design into actionable automation: plate maps, worklists, and instrument parameters generated rather than assembled by hand. On the quality side, define the controls and acceptance criteria that keep execution consistent plate to plate and month to month, and pursue experiments that identify where quality drifts so we can continuously raise the bar.

You will own: - The automation tooling used by the synthetic chemistry team: build it, refine it as they use it, and close the gap between what chemists want to run and what the instruments can execute. - What the data-generation step requires: specify what a condition set must prove before running at scale—plate design, controls, replication, and validation criteria. The chemist owns the reaction; you own whether the experiment is built so results are trustworthy in production. - Process QC in the data generation pipeline: the controls, anchors, replication, and plate-acceptance criteria that catch a bad run while it is still a bad run. - Execution quality once campaigns are running: whether a plate was run to standard, consistency across operators and decks and months, and what happens when a run fails partway through. - Finding where the process loses quality and fixing it: side experi