Chief Computational Scientist

Lifeline AI is at the forefront of transforming medicine through its cutting-edge platform, the Personalized Health Operating System (PH-OS), which transitions healthcare from reactive treatments to proactive, personalized therapies. By addressing the root causes of disorders, the platform eliminates AI hallucinations and validates molecular designs against rigorous biological and physical constraints. Lifeline AI’s revolutionary approach combines computational biology and safety-focused, scientific AI to bring a new standard to therapeutic discovery. With a strong commitment to improving healthcare outcomes, Lifeline AI is reshaping medicine with breakthrough technology and innovative solutions.

Role Description This is a full-time remote position for a Co-Founder & Chief Computational Scientific Officer (Principal Investigator) at Lifeline AI. This role is for an Equity based position: This is an equity-based only position at an early-stage startup, offering a significant ownership percentage in lieu of base salary.

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.

Role Description

This is a full-time remote role for a Lead Computational Physicist specializing in Tensor Networks and High-Performance Computing (HPC). The role involves leading research and development efforts, specifically modeling complex physical and biological systems using advanced computational physics techniques. The candidate will design, optimize, and validate algorithms for tensor network simulations applied to biological problems and utilize HPC architectures to ensure scalability. Collaborative problem-solving with interdisciplinary teams and contributing to scientific advancements in personalized healthcare are integral aspects of the position.

Your primary directive is to ensure our codebase remains mathematically pristine. You will work alongside the principal architect to optimize Python/JAX scripts, manage extreme-scale tensor contractions, mitigate numerical singularities, and ensure our continuous execution loops safely You will be leading research initiatives, directing computational and biological integration for therapeutic discovery, overseeing scientific innovation, and managing interdisciplinary teams. The candidate will also be responsible for ensuring that strategic projects align with the company’s vision to establish a new standard of scientific rigor and develop cutting-edge solutions in computational biology.

Key Responsibilities:

• Mathematical Auditing: Review and optimize complex Python scripts simulating potential energy surfaces, ensuring strict thermodynamic invariants are maintained.
• Tensor Network Optimization: Implement and scale out-of-core tensor network contractions (using libraries like cuTensorNet or JAX) across distributed architectures without triggering Volatile Random-Access Memory (VRAM) exhaustion.
• Numerical Stability: Engineer robust mitigations for catastrophic cancellation and floating-point rounding errors in high-order finite difference calculations.
• HPC Memory Management: Orchestrate dynamic memory slicing and asynchronous NVMe checkpointing to manage massive matrix states during continuous computational sweeps.
• Deterministic Guardrails: Ensure all algorithmic execution strictly avoids "hallucinated" stochastic variables, enforcing rigid mathematical boundaries at all times.

Qualifications

• Expertise in Computational Physics, Tensor Networks, and High-Performance Computing
• Strong background in Treatment Planning and Medical Physics
• Deep theoretical and applied understanding of Tensor Network States
• Proficient in Radiation Physics and its computational applications
• Experience in Research and Development (R&D), particularly in computational and scientific innovations
• Mastery in scientific programming languages (e.g., JAX, PyTorch, CUDA, Python, C , Fortran) and familiarity with HPC environments
• Ph.D. or post grad experience in Quantum Physics, Applied Mathematics, Quantum Information Science, Theoretical Computer Science, or a closely related field.Physics, Computational Biology, Applied Mathematics, or a related field
• Experience in the healthcare or life sciences domain is an advantage
• Proven experience managing out-of-memory computations, distributed systems (MPI), and hardware-aware algorithm design (GPU ALU optimization, PCIe bandwidth limits).
• Quantum Expertise: Deep theoretical understanding of quantum mechanics, quantum open systems, and quantum complexity theory.
• Mathematical Proficiency: Exceptional grasp of linear algebra, probability theory, tensor networks, and group theory.
• Analytical Thinking: Demonstrated ability to translate highly abstract mathematical concepts into workable computational models.
• Strong analytical and problem-solving skills with the ability to work collaboratively in interdisciplinary teams
• Expertise in Science and Physics with a strong foundation in computational or biological sciences.
• Proven experience in academic environments such as University Teaching, with the ability to mentor and lead cross-functional teams.
• Exceptional skills in Research with a track record of impactful publications or scientific contributions.
• Strong Project Management skills, including leadership, coordination of projects, and ability to meet objectives in high-stakes environments.
• Excellent communication skills and ability to translate complex scientific concepts for diverse audiences.

Preferred Qualifications

• Postdoctoral research experience or 10 years of industry experience in quantum computing.
• Hands-on experience running algorithms on actual quantum hardware (superconducting qubits, trapped ions, neutral atoms).
• Strong background in Quantum Error Correction (QEC) codes (e.g., surface codes, LDPC).
• Familiarity with classical machine learning techniques and quantum machine learning (QML) intersections.

To Apply:

Please submit your resume/CV. or reach out directly to Abraham@ProjectlifelineAI.com