AI Senior Scientist

Human data before the clinic Vivodyne accelerates the successful discovery, design, and development of human therapeutics through the convergence of novel biology, robotics, and AI. Our platform enables customers to massively de-risk drug candidates by testing them against functional, lab‑grown, human organs and multi‑organ systems. We conduct preclinical discovery and clinical development campaigns to generate new therapeutics and clinical strategies from these proprietary, physiologically-realistic human organ tissues at unprecedented scale, speed, and quality through automation and machine learning. We’re financially backed by some of the most selective and successful venture funds, and several “top 10” global pharmaceutical innovators have already partnered with us to strengthen and speed their therapeutic pipelines. To learn more visit us at Role The AI Team at Vivodyne tackles some of the hardest and most interesting challenges in science and engineering. With access to extraordinarily feature-rich and massive-scale Vivodyne human tissue imagery, we are advancing the frontiers of artificial intelligence and its applications in biology. We are building a portfolio of AI technologies to automate the discovery, development, and de‑risking of novel therapies using our unique technology platform, including single‑cell 3D phenomics/machine vision, multimodal (multi‑omic) translation, and reinforcement learning for robotic planning & study design, among others. As an AI Senior Scientist, you will leverage your deep expertise in developing large-scale models (including Transformer, Diffusion, and hybrid architectures) and Generative AI solutions to help turn our state‑of‑the‑art imaging and multi‑omics datasets into groundbreaking scientific insights. You will collaborate closely with biologists, engineers, and AI specialists to deliver robust, production‑ready algorithms and models that power Vivodyne’s high‑impact discoveries. This role will be based on‑site at our offices in Brisbane, California Responsibilities Scientific Innovation: Work closely with internal and external stakeholders to understand their scientific objectives and innovate new approaches that utilize Vivodyne’s massive‑scale imaging and multi‑omics datasets. Model Development: Lead hands‑on development of cutting‑edge machine learning models (Transformers, Diffusion, hybrid architectures, etc.) with a focus on image processing, image enhancement, and Phenomap embeddings. Data Pipeline Optimization: Build and deploy pipelines capable of scaling to petabyte‑scale data, ensuring robust MLOps practices on AWS or equivalent cloud platforms. Multimodal Integration: Explore and develop novel methods to incorporate multi‑omics data into imaging‑based Phenomaps, advancing the state of the art in multimodal phenotypic analysis. Collaboration & Communication: Partner with tissue engineers, microfluidics experts, and robotics engineers to refine data collection strategies, provide feedback on imaging system performance, and identify opportunities for improved AI‑driven solutions. Scientific Rigor & Leadership: Uphold scientific excellence through peer reviews, proper documentation, and clear communication. Drive a culture of continuous improvement, best practices, and adherence to rigorous research methodologies. Scalability & Efficiency: Emphasize modularity, composability, and performance efficiency in designing and implementing high‑throughput compute and analytics pipelines. Domain & Technical Growth: Remain current with the latest research trends in Generative AI, biomedical imaging, cloud computing, and data‑intensive training. Proactively share knowledge and mentor teammates to foster overall organizational expertise. Requirements and Expectations Scientific Excellence : Stay current with AI/ML research, especially in Generative AI, Transformers, Diffusion models, and multi‑modal architectures. Apply rigorous, data‑driven methods for sound outcomes. Hands‑On Leadership: Set high standards for model development and code quality, mentoring team members and fostering innovation. Accountability: Own model and infrastructure development from concept to deployment, delivering reliable, scalable solutions aligned with Vivodyne’s mission. Problem Solving & Adaptability: Develop creative solutions to novel research challenges, thriving in a fast‑paced, dynamic startup environment. Collaboration & Communication: Work cross‑functionally to align AI strategies with business goals, ensuring clear communication and consensus‑building. Delivering Results: Prioritize impactful research, using project management best practices to track progress, mitigate risks, and meet deadlines. Architecture & Coding: Design scalable, cost‑effective systems and produce clean, well‑documented code with continuous testing and governance compliance. Resource Optimization: Apply financial discipline to maximize the efficiency of compute, storage, and third‑party services. Team & Thought Leadership: Foster an inclusive environment, mentoring future leaders and representing Vivodyne in AI research and industry discussions. Qualifications Education & Experience PhD in Computer Science, Applied Mathematics, or a related field, or equivalent practical experience. Proven expertise developing and deploying advanced ML architectures (Transformers, Diffusion, multi‑modal models) in Generative AI settings. Demonstrated history of success with image processing and large‑scale model training. Technical Skills Strong knowledge of AWS or another major cloud provider for MLOps, big data processing, and scalable computing. Experience building models for petabyte‑scale datasets and understanding the associated architectural and operational complexities. Domain Expertise (Preferred) Familiarity with biological or biomedical imaging; advanced knowledge in multi‑modal Phenomaps, especially integrating imagery with multi‑omics data, is a major plus. Additional Desired Skills Prior experience with HPC, GPU clusters, or distributed computing frameworks. Comfortable with container orchestration and open‑source data engineering/ML frameworks. Soft Skills & Leadership Strong written and oral communication skills, with a proven ability to present complex technical topics to non‑experts. Demonstrated ability to thrive in ambiguous environments, driving clarity and direction with minimal supervision. San Francisco pay range

$220,000—$270,000 USD

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