Co-Founder & Chief Computational Scientific Officer (CCSO)

Job description Company Description

Lifeline AI is a pioneer in transforming medicine by addressing the limitations of traditional AI in therapeutic discovery with its innovative "glass box" solution.

Our safety-first AI engine ensures that generative molecular designs adhere to strict biological and physical constraints, offering scientific legitimacy and a transparent audit trail for every prediction.

At the h...eart of our work is the PH-OS (Personalized Health Operating System), designed to transition healthcare from symptom-based treatments to proactive, personalized therapeutic solutions.

By focusing on identifying root causes of diseases, Lifeline AI is setting a new standard in computational biology and revolutionary healthcare outcomes.

Role Description This is a full-time remote position for a Co-Founder & Chief Computational Scientific Officer (Principal Investigator) at Lifeline AI.

This is a Co-Founder / Late Co-Founder position.

We are looking for a partner, not an employee.

Currently, this role offers significant equity compensation only, with a transition to a competitive market salary contingent upon securing our initial seed funding or grant awards.

As the Principal Scientific Investigator, you will be directly instrumental in unlocking this capital.

This is a high-risk, high-reward opportunity for a visionary PhD/MD who wants to own a substantial piece of the "Glass Box" revolution in medicine.

In this pivotal role, you will serve as the company’s Principal Investigator (PI), leading all non-dilutive funding efforts.

Your primary focus will be identifying and authoring grant proposals (CPRIT, NIH, NSF, ARPA-H) and managing the application process from conception to award.

You will set strategic scientific goals, lead innovative research, and ensure our AI-powered computational biology advancements remain grounded in physiological reality.

You will also represent the organization externally to grant committees and scientific partners.

Technical Validation: Serve as the final authority on scientific code integrity; you will be responsible for peer-reviewing the team's scripts to ensure that AI-generated hypotheses are computationally sound and biologically plausible.

Grant-to-Code Alignment: Translate high-level grant objectives (NIH/NSF) into specific technical milestones for the engineering team, ensuring that our "Glass Box" deliverables meet the rigorous standards of federal auditors.

Hands-on PI Leadership: While this is a leadership role, you must be willing to "look under the hood" of our PH-OS to verify that the causal AI engine is correctly interpreting multi-omic interactions.

Expert-level proficiency in computational biology frameworks (e.g., Bioconductor, GATK, or custom Causal AI libraries).

Experience in Dry-Lab leadership, with a focus on transcriptomics, proteomics, or metabolomics data analysis.

Actively audit and validate Python/R scripts to ensure algorithmic outputs align with biological ground truths.

Design and oversee the computational pipeline for multi-omic data integration.

Lead the development of the 'Glass Box' logic by translating complex biological pathways into verifiable code structures

Ideal skills

Programming: Python (SciKit Learn, Pandas, OpenMM, NumPy, SciPy, TensorFlow), R, Matlab, shell scripting

Servers: AlphaFold/OpenFold, NVIDIA BioNeMo, Llayma

Cloud Computing: Google GCP, AWS, Azure,

Operating Systems: Windows, Linux, MacOS, Android

Other: GitHub, Docker, PyTorch, TensorFlow

Data Types: NGS, DNAseq, RNAseq, CHiPseq, TCRseq, single-cell, spatial-omics, medical imaging,

proteomics, TCRseq

Bioinformatics tools: GATK, Seurat, Nextflow, pathway analysis, network analysis, variant calling,

sequence alignment

Technical Skills: multi-omics data integration, systems biology modeling, single cell analysis, spatial

transcriptomics, experimental design, organoids culture, microfluidics, machine learning, deep learning,

large sequencing data processing, research software development, longitudinal data analysis, patient

stratification, large scale clinical datasets, EHRs, sensor signal processing

Licenses & certifications: Best Practices for New People Leaders, Managing Teams, Leading with Vision, HIPPA & SOC 2, and future CLIA compliance, Clinical Practice, Agile Foundations, Scrums,

Familiarity with FAIR data principles (Findable, Accessible, Interoperable, Reusable) and reproducible research standards

Qualifications

• Principal Investigator Experience: Demonstrated success serving as a PI on federal research grants (CPRIT/SBIR/STTR, R01, or equivalent).

• Grant Writing Mastery: Proven ability to author and secure significant funding from major bodies such as the NIH, NSF, or DOD.

• Scientific Leadership: Advanced degree (PhD or MD required) in computational biology, bioinformatics, biomedical engineering, or a related field.

• Communication: Exceptional ability to articulate complex scientific concepts to both grant committees and non-technical stakeholders.

• Tech-Forward Mindset: Deep understanding of AI applications in healthcare, specifically Causal AI and Multi-Omics.

• Strategic Vision: Experience moving a product from "Pre-Clinical Feedback" to commercial viability.

• Strong analytical skills: for scientific research, data analysis, and problem-solving

• Exceptional communication skills, with the ability to articulate complex concepts to diverse audiences

• Proficiency in sales and marketing strategies, particularly in the context of scientific and healthcare industries

• Proven expertise and experience in leading and conducting innovative research

• Track record of leadership in interdisciplinary teams and successfully managing projects

Preferred, but not necessary.

• FDA experience

• Molecular dynamics, peptide, cellular modality specific versatility

• Synthesis and Conjugation Strategies

• Mass Spectroscopy understanding

• Toxicity in silico simulation

Industry

• Deep-Biotechnology Research

Employment Type

Full-time Show full description