Bioinformatics Engineer

Becoming•San Francisco, CA

About The Position

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 a Bioinformatics Engineer to design and own data systems that translate high-dimensional biological measurements into structured, usable intelligence. This is not a support analyst role. You will build computational pipelines and data architectures that integrate directly into Becoming’s predictive models and experimental platform. You will operate at the interface of wet lab biology, machine learning, and systems engineering. You will define data standards, build scalable pipelines, and take responsibility for signal integrity from raw measurement to model-ready representation. High agency is required. You will identify bottlenecks, design infrastructure, and own outcomes.

Requirements

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
Degree in bioinformatics, computational biology, computer science, or equivalent demonstrated depth
Experience building and maintaining biological data pipelines
Strong programming skills (e.g., Python)
Experience working with high-dimensional biological datasets
Familiarity with version control, containerization, and reproducible workflows
Demonstrated ability to turn ambiguous biological questions into structured computational outputs
Comfort operating without heavy pre-built platform abstraction

Nice To Haves

Experience integrating multi-modal datasets
Experience designing data systems for predictive modeling
Work on longitudinal or time-series biological data
Infrastructure-first mindset rather than analysis-first mindset
Bias toward robustness and reproducibility

Responsibilities

End-to-end ownership of biological data pipelines
Processing and QC of high-dimensional datasets (e.g., transcriptomic, imaging-derived, or multi-modal data)
Scalable workflows for ingestion, normalization, annotation, and versioning
Data models that support predictive and longitudinal analysis
Integration of experimental metadata with biological readouts
Reproducible computational infrastructure (cloud or on-prem)
Validation frameworks for data integrity and drift detection
Documentation and standards that enable scaling across teams