Staff Engineer, Chemical Search
Listed on 2026-08-20
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Software Development
AI Engineer (Applied/Software)
Satomic’s mission is to close the gap from idea to molecule with faster navigation of chemical space. We are building an automated chemistry platform that integrates laboratory robotics, software, and AI to transform small-molecule synthesis and drug discovery.
Satomic has raised a $15M Seed from fantastic investors including Riot Ventures, HOF Capital, and Compound VC, and our team includes alumni from leading large pharmas, small biotechs, and AI labs.
This role is critical to Satomic’s mission of closing the gap from idea to molecule by building the chemistry search engine that lets our customers see, query, and reach into Satomic’s accessible chemical space: trillions of multistep-accessible molecules grounded in the platform’s actual synthesis capabilities. As Satomic builds toward an “API to Atoms,” this role ensures that the interface our partners query against is reliable, low-latency, and grounded in the real synthetic capabilities of the platform.
The medicinal chemists, computational scientists, and generative-model-driven design teams will depend on the product of your work to navigate chemical space as they solve the most important challenges in medicine and human health.
About the role
This first cheminformatics hire will design and build the chemistry search engine: the system that exposes our accessible chemical space through query modes fit for how our partners actually design molecules and run their discovery campaigns. This engine will be searched by generative models, AI agents, and medicinal chemists alike. The engine is grounded in predictive models that capture the actual capabilities of our platform rather than a static catalog, which changes the underlying architecture in ways an experienced builder in this space will already have opinions about.
You will take in constraints from our automation team, conversion and substrate-scope models from our ML team, and building blocks from our vendors to define the complete specification of Satomic's accessible chemical space. You will also translate insights from our partners’ discovery strategies into fit-for-purpose query modes, keeping the engine in sync as both Satomic's accessible chemistry expands and our partners' discovery workflows evolve.
This role is a founding-engineer position within the Development team. You'll be the technical owner of the cheminformatics domain — defining its standards and its architecture — while working as part of a broader engineering group that owns the platform you're building on. You will partner closely with our data science / ML lead and work day-to-day with the rest of Development on the systems your engine integrates with.
You will have exposure to end users at Satomic’s commercial partners to translate user behavior into the query types the engine should support. We believe being part of the chemistry-AI conversation externally (through publication, open source, and the conferences and forums where this field develops) is part of the job.
This is a hands-on role for an engineer who thrives with extreme latitude, ownership, and judgment. You will decide what to build versus buy on cheminformatics-specific tooling (RDKit / Open Eye for chemistry primitives, Ai Zynth Finder / ASKCOS / Synthia / IBM RXN for retrosynthesis, pharmacophore tooling, IP / FTO filtering, vector index choice), choose the engine's core representations, and define the versioning rules that keep it correct as chemistry capabilities expand.
Infrastructure-adjacent choices — vector index, storage layer, serving framework — are made jointly with the team.
As Satomic’s first cheminformatics engineer, you will be the technical owner of this domain: you will be the sole individual contributor driving this work directly for the first several months, with the opportunity to build a team under you within year one as the surface area grows.
Expected Outcomes
By day 30, you will have:
- Identified the open architectural decisions for v1 (internal representation, query modes, inference layer, build-vs-buy on retrosynthesis tooling, versioning strategy) and written your prioritization rationale clearly enough to be debated and built…
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