Powerhouse Biology, Inc. is seeking an Applied ML Scientist to build computational frameworks for multi-modal biological data integration to develop precision protein and peptide therapeutics for age-related diseases driven by mitochondrial dysfunction.
Requirements
- Strong Python and modern ML frameworks; comfortable with at least two of: computer vision, tabular/omics modeling, graph methods.
- Solid statistical instincts (splits, controls, basic calibration) and an eye for clean, reproducible work.
- Demonstrated expertise working with high-content cell culture image data and at minimum one type of omics dataset in an independent or lead capacity, such as driving analysis pipelines, integrating multi-omics data, or publishing results.
- Skilled with network modeling, graph algorithms, and computational approaches to systems biology, including building, analyzing, and interpreting biological graphs.
- Familiarity with cloud computing and high-performance computing environments.
- Experienced in software development best practices, including version control (e.g. Git), code review, testing frameworks, workflow orchestration, and CI/CD pipelines.
- Prior work translating model interpretability into experimental design changes.
Responsibilities
- Design and maintain multi-modal ML workflows for ingestion and processing of imaging and omics data.
- Write reliable, readable code; document assumptions; version data and models; contribute to simple, durable team standards.
- Help shape the technical culture through thoughtful code review, knowledge sharing, and pragmatic tooling choices.
- Partner with the experimental team to frame questions, scope datasets, and plan iterative validation to translate computational insights into real-world therapeutics.
- Compose and evaluate vision, sequence, and graph models with clear statistical judgment.
- Encode mechanistic priors grounded in mitochondrial biology.
- Iterate quickly with prospective feedback from the lab.
Other
- Quantitative background (CS, applied math, physics, engineering, computational biology, or similar) with 2-5+ years of hands-on ML for scientific or complex data.
- Comfortable working in a collaborative, interdisciplinary environment, integrating computational methods with experimental biology.
- Able to balance research and engineering, delivering robust computational tools while exploring novel methodologies.
- Effective at communicating in multi-disciplinary teams where you are simultaneously the expert and the apprentice.
- Knowledge of mitochondrial biology.
