We are hiring one Computational Biologist, and we are considering candidates in both Boston and London.

Responsibilities

• Multi-omic data analysis and interpretation. You own the computational analysis of datasets generated by our wet lab (metagenomics, metabolomics, 16S), from QC and feature extraction through statistical analysis and biological interpretation. 

• Computational studies. You run your own proof-of-concept investigations: microbiome-compound interactions, community dynamics, predictive biomarkers. You're a scientist with hypotheses, not a technician with a task list.

• Perturbation data quality. You own the validation layer between the bench and the models. Experimental drift, contamination, batch effects, protocol deviations: you catch them before they corrupt model training.

• Reproducible analytical infrastructure. As methods mature, you convert ad hoc analyses into robust, documented, version-controlled pipelines that others can run and extend. Your workflows become the standard.

• Wet-dry lab bridge. You partner with the wet lab team to design experiments with computational endpoints in mind, and with the data engineering and ML teams in London to ensure smooth data handoff. 

Your Background

• PhD in computational biology, bioinformatics, microbial genomics, or a related quantitative life-science field; or MSc with 3+ years of relevant industry experience.

• Hands-on experience analysing microbiome or multi-omic data (metagenomics, metabolomics, 16S) using common bioinformatics tools and pipelines, with strong programming skills in Python or R.

• Track record of independent scientific work, demonstrated by publications, preprints, or equivalent outputs.

• Ability to write clean, version-controlled, reproducible code; comfort with Git, Linux/command-line environments, and cloud computing basics.

• Statistical rigor: multiple-hypothesis correction, compositional data analysis, batch effects. You know when a result is real vs. noise.

• Experience working effectively in a startup or other fast-moving, resource-constrained environment.

Nice to Have

• Experience building reproducible pipelines with Nextflow, Snakemake, or similar.

• Familiarity with machine learning concepts and comfort collaborating with ML engineers on feature engineering or model evaluation.

• Experience with metabolomics data processing or LC-MS-based workflows. (This is a significant focus area for us.)

• Experience working closely with wet lab teams to co-design experiments with computational endpoints.

• Contributions to open-source bioinformatics tools or community resources.

Why Join Outpost Bio?

• You'll own real equity in what you build. We offer meaningful ISO stock options because we believe the people building this company should share in what it becomes. We want teammates who think like owners, and we structure compensation to reflect that.

• Outstanding benefits. Full medical, dental, and vision coverage from day one (Outpost covers 100% of employee premiums). 401(k) with match. 25 days PTO plus your birthday off. Short- and long-term disability.

• An ML Lab-in-the-loop. You'll run your own studies, publish findings, and see your analytical work feed directly into frontier ML models. The feedback loop between your analysis and the wet lab and AI platform is measured in days, not years: your data processing choices ripple through model performance, and you'll iterate together.

• Small team, outsized reach. You're joining a small founding team backed by top-tier investors with deep connections across AI and Bio. The science you do here will directly shape how pharma and consumer companies understand molecule-microbiome interactions.

About Us

Outpost Bio pairs high-throughput functional assays of human-derived microbial communities with causal machine learning to build predictive models of microbiome-compound interactions. Unlike black-box approaches, our Lab-in-the-Loop process tightly couples experimental design and AI development, so the models inform the science and the science informs the models. We generate large-scale perturbation datasets using stool-derived communities (SDCs) and multi-omic measurements, then train frontier models that predict metabolism, toxicity, and response at the community level. Headquartered in Boston and London, backed by Seedcamp, Merantix, Defined, and Openseed VC.