Senior Computational Biologist at Asimov

Boston, Massachusetts, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Biotechnology, Synthetic BiologyIndustries

Requirements

Ph.D. in Bioengineering, Computational Biology, Computer Science, Applied Mathematics, or a related quantitative field
Proficient in sequence-based analysis and hands-on experience integrating large-scale omics datasets (e.g., NGS, RNA-seq, proteomics) to inform model development and validation
Expert in applying machine learning and advanced mathematical techniques (e.g., dynamical systems, differential equations, stochastic modeling) to biological systems
At least 3 years of experience in a pharmaceutical or biotechnology industry setting
Strong background in modeling dynamic biological systems, such as gene regulatory networks, metabolic pathways, or cell population dynamics
Skilled programmer, fluent in Python and associated scientific computing/ML libraries
Excel at communicating complex scientific and mathematical concepts effectively to colleagues with diverse backgrounds and expertise
Collaborative, impact-driven scientist, passionate about working in a fast-paced, research-focused environment to engineer biology

Responsibilities

Design and implement sophisticated models—including data-driven (ML/AI), mechanistic (e.g., ODE-based), and hybrid approaches—to predict and optimize the performance of biologics and vector manufacturing processes
Develop models that predict functional outcomes such as transgene expressibility, protein stability, and product quality attributes from nucleic acid and amino acid sequence features
Develop new models to predict developability attributes such as expressibility, secretability, aggregation, and stability of biologics, from sequence
Create multi-scale models of cellular behavior, integrating omics data (RNA-seq, proteomics, etc.) to simulate gene expression, protein secretion dynamics, metabolism, and cell phenotype
Work in close collaboration with bench scientists to guide rational experimental design, using techniques like Bayesian Optimization to ensure that data generation is maximally informative for modeling and yields actionable insights
Contribute to long-term computational strategy by exploring novel algorithms, modeling frameworks, and data integration techniques to solve critical challenges in synthetic biology and therapeutic development

Skills

Computational Biology

Machine Learning

Applied Mathematics

ODE Modeling

RNA-seq

Proteomics

Omics Data

Sequence Modeling

Predictive Modeling

Bioprocess Optimization

Asimov

Synthetic biology solutions for biopharmaceuticals

About Asimov

Asimov operates in the synthetic biology field, providing a combination of cells, genes, and software to assist in advanced genetic design. Their products include cloud-based software that allows clients in the biopharmaceutical industry to design, simulate, and optimize genetic systems. Additionally, they offer engineered GMP host cells that are used for therapeutic applications. Asimov distinguishes itself from competitors by guaranteeing high titer cell lines for monoclonal antibodies with a promise of '4 g/L or it's free,' which reduces risk for their clients. The company's goal is to enable biopharma companies to produce high-quality protein therapeutics and scalable viral vectors efficiently, supported by their in-house expertise in synthetic biology and process development.

Boston, MassachusettsHeadquarters

2017Year Founded

$199.1MTotal Funding

SERIES_BCompany Stage

AI & Machine Learning, BiotechnologyIndustries

51-200Employees

Risks

Competition from LatchBio's code-free biocomputing infrastructure challenges Asimov's software offerings.

Integration of The Foundry may lead to potential cultural clashes and operational disruptions.

Rapid data generation in synthetic biology may strain Asimov's data processing capabilities.

Differentiation

Asimov offers a '4 g/L or it's free' promise for monoclonal antibodies.

The LV Edge Producer System eliminates GMP plasmid costs and reduces process complexity.

Asimov integrates synthetic biology, computer-aided design, and machine learning for genetic design.

Upsides

Stable cell line development reduces costs and increases scalability in lentiviral production.

Acquisition of The Foundry enhances Asimov's expertise in genetic design and engineering.

Growing biotech investments indicate a robust market for Asimov's engineered cell lines.

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