Research Scientist, Analytical Chemistry

Onepot•South San Francisco, CA

10h•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 is the feedback signal of that loop. Every reaction the platform runs ends as a measurement, and those measurements are what our models learn from — which makes this the role where the platform's notion of what worked gets defined. We are building a small, unusually ambitious team and are looking for an analytical chemist who wants to own that definition. Most analytical work optimizes one method for one molecule. Ours has to hold up across a platform that runs broad, structurally diverse chemistry in parallel — compounds that ionize strangely, products that hide behind adducts and in-source fragmentation, regioisomers that share an exact mass, detectors that saturate, peaks that are not what they claim to be. Interpretation has to work at scale, and it has to be right. And the data has two customers: people and models. A number that leaves the building goes to a customer with our name on it, and the same number becomes training data for the next generation of our models. No one has built an interpretation layer that holds up across chemistry this diverse — the tools do not exist off the shelf, and building them is most of why this job is interesting.

Requirements

Ph.D.-level depth in analytical chemistry or a related field, or the demonstrated equivalent.
A track record of solving structures others could not: unknowns without reference standards, unexpected products, trace impurities — with the orthogonal evidence to prove the assignment.
Experience taking an analytical capability from an initial need to something that runs routinely — method, instrument, and data flow.
Experience interpreting analytical data in volume, where the throughput forced you to systematize your own judgment rather than eyeball everything.
Evidence that you automate yourself out of repetitive work — scripts, decision rules, or models that replaced something you used to do by hand.
Expert small-molecule mass spectrometry: rationalize fragment series mechanistically, defend elemental composition from mass defect, isotope fine structure, and ring-and-double-bond logic. Understand electrospray effects and quantitative signal extraction from saturated detectors.
Deep NMR: routine command of 2D suite (COSY, HSQC, HMBC, NOESY), de novo assignment of unfamiliar scaffolds and mixtures, relative stereochemistry from NOE and coupling analysis, and quantitative NMR against an internal standard. Experience with heteronuclear work (¹⁹F, ³¹P, ¹³C), variable-temperature experiments, in situ reaction monitoring, and DOSY on mixtures.
Separation science at the method-development level: stationary-phase and mobile-phase selection from first principles, gradient design, and chiral or SFC methods when a problem calls for them.
Quantitation and statistics you can defend: calibration design, internal standards, ion suppression and matrix effects, limits of detection — and replicates, noise floors, and distributions rather than single traces.
Fluency with AI agents and modern AI tooling, and a habit of prototyping quickly.
Obsessive attention to detail.

Nice To Haves

Experience in a startup, research group, or other environment where you had significant ownership and limited resources.
Impurity and degradant identification, or structure elucidation of unknowns at trace level.
Ion mobility, multi-stage MS, or other advanced acquisition strategies.
Two-dimensional or otherwise specialized separations — SFC, chiral, 2D-LC.
NMR of complex mixtures and quantitative NMR.
Mass-spectrometry informatics — spectral processing, open formats, or ML applied to spectra.
Designing QC and system-suitability schemes for analytical workflows.

Responsibilities

Own analytical data quality end to end — the methods, the interpretation standards, and the pipelines that apply them to everything the platform runs.
Take on the interpretation problems that automation cannot yet handle: assigning fragmentation that no library contains, resolving ambiguous identity, deciding whether a marginal result is chemistry or artifact.
Decide when a question needs orthogonal evidence — NMR, accurate mass, a separation we do not run yet — and go get it.
Evaluate, propose, and integrate new techniques or methods (e.g., SFC, improved NMR utilization) into our systems.
Turn your judgment into scalable solutions like decision rules, scripts, or models so that your standards apply to every well the platform produces, around the clock.
Work with AI agents and the ML team to push more of the interpretation into software.
Ensure work is finished as a tested module, an endpoint, or a button in the app, not a notebook or a ritual only you can perform.
Automate routine parts of the job, focusing on applying judgment and building scalable solutions.