Omics & Bioinformatics Scientist; Wet Lab + Data Pipelines

About Becoming

Becoming is building Developmental Intelligence: AI for predicting how organisms change over time.

Most experimental systems fail when metabolic demands become too high. We are building systems that don’t — by combining engineered metabolic environments, sensing, control, and software into tightly integrated products that operate reliably over long time horizons.

Hardware is core to our platform. It must work continuously, predictably, and under real biological constraints.

We are hiring an Omics & Bioinformatics Scientist (Wet Lab + Data Pipelines) who can operate across omics experiment generation, perturbation experiments, computational analysis, and biological data engineering.

This is not a purely computational role. You will run omics experiments at the bench, generate high-quality datasets, and build the pipelines that convert raw biological data into structured datasets usable by machine learning systems.

The role spans transcriptomics, epigenomics, and proteomics. You will design and execute omics experiments, analyze the resulting datasets, and build reproducible data pipelines so these datasets can be integrated with imaging, physiological, and experimental metadata from Becoming’s systems.

You will work closely with developmental biologists generating engineered developmental systems and with AI researchers building predictive models of development. Your responsibility is to ensure that biological data is experimentally robust, computationally analyzable, and programmatically accessible.

This role requires someone comfortable owning the full lifecycle of biological data: experimental design → omics generation → analysis → pipelines → structured datasets.

• Design and execute omics experiments, including sample preparation and sequencing or mass-spectrometry library generation
• Perform wet-lab protocols for single-cell RNA-seq, bulk RNA-seq, ATAC-seq, spatial transcriptomics, and proteomics assays
• Design and run perturbation experiments (e.g., genetic, chemical, environmental) to probe developmental systems
• Prepare samples for mass spectrometry-based proteomics (e.g., LC-MS/MS workflows)
• Develop and maintain bioinformatics pipelines for processing omics datasets
• Perform quality control, normalization, and downstream analysis of transcriptomic and proteomic datasets
• Map cell states and developmental trajectories across time
• Work with experimental teams to design experiments that generate informative, analyzable datasets
• Build data pipelines to process, store, and structure biological datasets
• Integrate omics data with imaging, physiological measurements, and experimental metadata
• Structure datasets so they can be used directly by machine learning and modeling systems

You are someone who:

• Operates with high agency — you identify problems, define solutions, and execute
• Takes end-to-end ownership of what you build
• Brings high energy to complex, ambiguous engineering challenges
• Acts with high integrity — you are honest about tradeoffs, risks, and failure modes
• Communicates directly and clearly, especially when something won’t work
• Is self-aware about your strengths and gaps, and proactively fills them
• Thinks like a systems integrator, not a narrow specialist
• Cares deeply about understanding systems at a first-principles level

PhD in Genomics, Bioinformatics, Computational Biology, Systems Biology, Proteomics, or related field

• Demonstrated experience performing omics experiments at the bench (library preparation or proteomics sample preparation)
• Experience designing or executing biological perturbation experiments
• Experience analyzing transcriptomics or single-cell omics datasets
• Familiarity with proteomics workflows and mass-spectrometry datasets
• Proficiency in Python for biological data analysis
• Experience with tools such as Scanpy, Seurat, Bioconductor, or proteomics analysis software
• Ability to design experiments and interpret results in collaboration with experimental scientists

• Experience with LC-MS/MS proteomics workflows and analysis
• Experience integrating multi-modal datasets (transcriptomics, proteomics, imaging, physiology)
• Experience building data pipelines or data infrastructure for biological datasets
• Experience with developmental biology systems (embryos, stem cells, organoids, or early developmental models)
• Experience working with large genomics or proteomics datasets

• Competitive salary and meaningful equity
• Full benefits
• High-trust, high-ownership environment
• Rapid growth in scope and responsibility