Research Scientist, Analytical Chemistry

Onepot•South San Francisco, CA

5d•$180,000 - $240,000•Onsite

About The Position

onepot is automating chemistry with the goal of enabling a self-improvement loop for chemistry by combining AI and advanced robotics. In this loop, AI systems design experiments, robotic systems execute them, and the resulting data improves the next generation of models. Analytical chemistry serves as the feedback signal for this loop, where measurements from every reaction become training data for AI models. This role is crucial for defining the platform's understanding of successful outcomes. The position requires an analytical chemist who can own this definition and ensure data quality across a diverse range of parallel chemical reactions. Unlike traditional analytical work that optimizes a single method for a single molecule, this role demands methods that can handle broad, structurally diverse chemistry, including compounds with unusual ionization, fragmentation, and co-eluting species. The data generated has two critical consumers: external customers and internal AI models. The role involves building interpretation layers that can handle this diversity, as off-the-shelf tools are insufficient. The successful candidate will own analytical data quality end-to-end, including methods, interpretation standards, and data pipelines. This involves tackling complex interpretation problems that automation cannot yet solve, such as assigning unknown fragmentations, resolving ambiguous identities, and distinguishing between genuine chemical results and artifacts. The role also requires deciding when orthogonal evidence (e.g., NMR, accurate mass, specialized separations) is needed and obtaining it. If the platform lacks necessary techniques or methods, the candidate is expected to evaluate, propose, and integrate them. A key aspect of this role is to translate expert judgment into scalable solutions like decision rules, scripts, and models, enabling automated interpretation of all platform-generated data around the clock. Collaboration with AI agents and the ML team to enhance software-based interpretation is expected. The ultimate goal is to create robust, reusable analytical modules and endpoints, rather than relying on manual processes.

Requirements

Ph.D.-level depth in analytical chemistry or a related field, or demonstrated equivalent experience.
A track record of solving structures others could not, including unknowns without reference standards, unexpected products, and trace impurities, with orthogonal evidence to prove the assignment.
Experience taking an analytical capability from an initial need to something that runs routinely, including method, instrument, and data flow.
Experience interpreting analytical data in volume, necessitating systematization of judgment over manual analysis.
Evidence of automating repetitive work through scripts, decision rules, or models.
Experience in a startup, research group, or similar environment with significant ownership and limited resources.
Expert small-molecule mass spectrometry skills, including mechanistic rationalization of fragment series and defending elemental compositions using mass defect, isotope fine structure, and ring-and-double-bond logic.
Deep NMR skills, including routine command of 2D NMR techniques, de novo assignment of unfamiliar scaffolds and mixtures, relative stereochemistry determination, and experience with heteronuclear NMR, variable-temperature experiments, in situ reaction monitoring, and DOSY on mixtures.
Separation science skills at the method-development level, including stationary-phase and mobile-phase selection, gradient design, and chiral or SFC methods.
Proficiency in quantitation and statistics, including calibration design, internal standards, ion suppression and matrix effects, limits of detection, and understanding of replicates, noise floors, and distributions.
Fluency with AI agents and modern AI tooling, with a habit of rapid prototyping.
Obsessive attention to detail.

Nice To Haves

Impurity and degradant identification, or structure elucidation of unknowns at trace level.
Experience with ion mobility, multi-stage MS, or other advanced acquisition strategies.
Experience with two-dimensional or specialized separations like SFC or 2D-LC.
Experience with NMR of complex mixtures and quantitative NMR.
Experience with mass spectrometry informatics, including spectral processing, open formats, or ML applied to spectra.
Experience designing QC and system-suitability schemes for analytical workflows.

Responsibilities

Own analytical data quality end to end, including methods, interpretation standards, and pipelines.
Take on interpretation problems that automation cannot yet handle, such as assigning unknown fragmentations, resolving ambiguous identities, and deciding whether a marginal result is chemistry or artifact.
Decide when a question needs orthogonal evidence (e.g., NMR, accurate mass, a separation) and obtain it.
Evaluate, propose, and integrate new techniques or methods into the platform's data systems.
Turn expert judgment into scalable solutions like decision rules, scripts, and models for automated interpretation.
Work with AI agents and the ML team to push more interpretation into software.
Develop tested modules, endpoints, or application features for analytical data interpretation.