Research Engineer (Molecule Design Platform) chez Genbio

Headquartered in Silicon Valley, we are a newly established start-up, where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of Generative AI. Our team comprises leading minds and innovators in AI and Biological Science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our foundation model training leading to groundbreaking advancements and a transformative approach to healthcare. With headquarters in Silicon Valley, California, and a branch office in Paris, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.

Key Responsibilities

Design, develop, and optimize machine learning inference and training pipelines for molecular and biological data.

Implement and execute large-scale hyperparameter searches to optimize model performance across molecule design tasks.

Productionize ML models including packaging, containerization, and scalable deployment.

Build, deploy, and maintain APIs and services for model inference and integration with downstream tools and data systems.

Ensure scalability, observability, and reproducibility across all ML workflows.

Collaborate closely with research scientists and data engineers to translate model prototypes into reliable production systems.

Maintain high engineering standards through testing, documentation, and CI/CD practices.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field and 2+ years of industry experience

Proficiency with Docker, Kubernetes, and PyTorch/PyTorch Lightning.

Strong software engineering foundations, including version control, testing, and code quality practices.

Hands-on experience developing and deploying APIs for ML inference.

Experience scaling distributed training or inference pipelines in production.

Strong communication and collaboration skills in a fast-paced, interdisciplinary environment.

Preferred Qualifications

Experience with orchestration and CI/CD tools such as Ray, Kubeflow, or ArgoCD.

Familiarity with GraphQL, RESTful API design, and cloud infrastructure (AWS, GCP, or OCI).Prior experience optimizing inference code for large-scale models or biological data.

Understanding of biological data modalities or molecular representation learning is a plus.

Industry experience deploying ML systems in production environments.

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