About Acrobat Genomics
Acrobat Genomics is combining its expertise in high-throughput screening, computational biology, and gene editing to develop tools for precise genetic control. We're expanding our computational capabilities to address multiple projects spanning therapeutic development and biosecurity applications.
Position
As Senior Data Scientist, you'll lead the development of our computational platforms across multiple projects and technology areas and will work closely with our wet lab scientists to drive the development of our core technology platform.
Responsibilities
Create novel approaches for biological design, optimization, and functional prediction
Collaborate with wet lab scientists to validate computational predictions
Oversee data infrastructure, pipelines, and computational workflows
Contribute to strategic planning and technology roadmap development
Support business development through technical contributions to funding proposals and partnerships
Required Qualifications
MS or PhD (or equivalent experience) in Computational Biology, Computer Science, Bioinformatics, or related field
Proven track record in developing computational methods for bioengineering
Experience with machine learning approaches for biological sequence and structure analysis
Strong programming skills in Python, with experience using frameworks like PyTorch, TensorFlow
Demonstrated ability to translate complex biological problems into computational solutions
Background in protein structure prediction, molecular modeling, or related fields
Experience leading technical teams and driving collaborative projects
Excellent communication skills and ability to work across computational and experimental domains
Why you might be a good fit
Knowledge of gene editing technologies and control mechanisms
Experience with protein-protein interaction modeling
Track record of publications in relevant computational biology or machine learning venues
Prior experience in biotechnology startup environment
Experience with high-performance computing and large-scale data analysis using cloud service providers
Skills Required
- MS, PhD, or equivalent experience in Computational Biology, Computer Science, Bioinformatics, or a related field
- Proven track record developing computational methods for bioengineering
- Experience with machine learning approaches for biological sequence and structure analysis
- Strong programming skills in Python
- Experience with PyTorch or TensorFlow
- Ability to translate complex biological problems into computational solutions
- Background in protein structure prediction, molecular modeling, or related fields
- Experience leading technical teams and driving collaborative projects
- Excellent communication skills and ability to work across computational and experimental domains
- Knowledge of gene editing technologies and control mechanisms
- Experience with protein-protein interaction modeling
- Publications in relevant computational biology or machine learning venues
- Prior experience in a biotechnology startup environment
- Experience with high-performance computing and large-scale data analysis using cloud service providers
What We Do
Acrobat Genomics is a privately held, San Carlos, California biotechnology company and Public Benefit Corporation building the functional data layer for AI-driven protein and genome engineering. It trains AI models on high-throughput functional screens measured directly in human cells, combining that data with machine learning to design novel therapeutic proteins and genetic medicines for rare disease, and to support biosecurity applications. Its stated mission is to empower researchers and healthcare professionals to unlock genomic data and improve health outcomes.
