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Substrate Bio

Autonomous AI-driven wet labs for biological experimentation

Member of Scientific Staff - Computational Chemistry

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Grounding in synthetic organic chemistry sufficient to communicate with synthetic chemists, elicit and distill their expertise, and translate practical chemical judgment into computational rules, models, and workflows.
  • Strong cheminformatics skills, including rigorous digital handling of chemical structures, molecular and reaction representations, SMILES and SMARTS, stereochemistry and isomers, salts, tautomers, protonation and charge states, substructure and similarity searching, molecular descriptors, and chemical identity and registration.
  • Experience with retrosynthesis or computer-aided synthesis planning, developing or evaluating methods that generate, evaluate, and rank synthetic routes rather than simply calling an off-the-shelf tool.
  • Ability to reason computationally about reactions and their feasibility, including transformations and templates, reagents, conditions, selectivity, and supporting reaction evidence.
  • Ability to plan across many targets, identify shared chemistry, common intermediates, and building blocks across hundreds or thousands of molecules, and reduce them to a manageable set of synthetic strategies.
  • Working grasp of trade-offs among route quality, synthetic tractability, compound diversity and value, building-block availability, cost, step count, and throughput.
  • Experience with RDKit or equivalent cheminformatics tooling, chemical databases, APIs, and large chemical datasets.

Responsibilities

  • Build the computational chemistry capability from the start, owning the process from desired target molecules through synthesis plans executable by automated equipment and the representation and registration of compounds.
  • Work with synthetic chemists and automation scientists and engineers to convert chemists’ judgment into repeatable computational rules, models, and workflows.
  • Plan routes that account for platform capabilities and constraints, available building blocks and reagents, supported reaction classes and conditions, handling constraints, work-up, purification, and throughput.
  • Analyze target sets to find shared chemistry, common intermediates, and building blocks, and reduce them to a manageable set of synthetic strategies while balancing diversity and compound value against synthetic cost.
  • Define with the automation team what synthesis plans must specify for downstream systems, and connect planning, synthesis, and experimental results so results improve subsequent plans.
  • Establish chemical identity rules, including when structures represent the same entity, and consistently handle stereoisomers, salts, tautomers, protonation, charge states, representation, and normalization.
  • Choose methods, set standards, write production code, and shape the growth of the computational chemistry capability.
  • Learn the synthesis platform’s capabilities and constraints and agree with synthetic chemists on rules for chemical identity, structure normalization, validation, deduplication, stereochemistry, isomers, salts, tautomers, and charge states.
  • Ingest vendor catalogues and building-block inventories through supplier APIs and external chemical data sources, capturing availability, cost, and metadata as planning constraints.
  • Set up compound-registration workflows and identifiers for downstream systems.
  • Run an initial planning pass on a real target set, generating, evaluating, and ranking routes and grouping targets by shared chemistry, intermediates, and building blocks.
  • Incorporate platform capabilities and constraints into planning so routes are evaluated for executability as well as chemical plausibility.
  • Select among target-set options using diversity and compound value alongside synthetic cost, step count, and throughput.
  • Connect compounds, reactions, and experimental results to the wider digital chemistry stack, including ELN, LIMS, and compound-registration systems, and define how results feed back into route and reaction models.

Desired Qualifications

  • Machine learning or artificial intelligence approaches to reaction prediction, condition recommendation, or retrosynthesis.
  • Reaction-condition prediction or optimization and high-throughput experimentation design.
  • Using experimental reaction data to improve models or planning decisions.
  • Methods for selecting compounds or routes across large libraries, such as diversity selection, synthetic-cost models, and multi-objective optimization.
  • Integrating scientific software with laboratory automation and with ELN, LIMS, or compound-registration systems.
  • Production-quality scientific software, APIs, databases, and data pipelines.

About the Company

Substrate Bio

Autonomous AI-driven wet labs for biological experimentation

Substrate Bio operates AI-native data-production infrastructure for biology, connecting computational models with real-world experimentation. The company runs a network of autonomous, robotics-powered wet labs, starting in London with a second planned for San Francisco, running standardized experiments with full provenance, metadata, and quality control. Researchers access the labs via API, submitting experiment parameters and receiving structured data and metadata, while foundation models help design assay selections, cell systems, and gene panels. What sets Substrate Bio apart is its closed feedback loop, where better data improves its models and better models design better experiments. The goal is to make biological experimentation as programmable and scalable as computation itself.

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