At Element Biosciences, we are passionate about our mission to empower the scientific community with more freedom and flexibility to accelerate our collective impact on humanity. We have built a highly efficient product-driven organization where employees can learn, grow, and thrive in a challenging but encouraging environment. We are committed to scientific integrity, collegiality, honesty, objectivity, and openness.

We are seeking a highly skilled and motivated Senior Engineer, Machine Learning to join our dynamic team. The ideal candidate will have experience in data science and machine learning, with a background in working with multiomic and single-cell data and/or image processing and computer vision. This role involves creating, exploring, and analyzing models, as well as applying advanced image processing techniques to drive our research and development efforts and contribute to critical programs.

This role will report to Vice President, AI and will be an onsite role at our headquarters in San Diego. If you possess the following and want to make a meaningful impact, we invite you to explore this role.

Responsibilities

: Design, develop, and optimize deep learning models (CNNs, Vision Transformers, U-Net variants, and related architectures) for biological image analysis and classification Deploy and maintain production-grade neural network models on cloud infrastructure (e.g., AWS) or directly on imaging instruments, ensuring reliability, scalability, and performance Apply advanced image processing and computer vision techniques to analyze multimodal biological images, including segmentation, feature extraction, and quality scoring Develop and manage end-to-end ML pipelines — from data ingestion and preprocessing through model training, validation, and inference Analyze and interpret single-cell and multiomic data to support biological context and downstream interpretation of imaging results Collaborate with cross-functional teams including biology, software engineering, and instrumentation to co-design experiments and translate biological

Requirements

into modeling objectives Explore and analyze large-scale imaging datasets to identify patterns, failure modes, and opportunities for model improvement Communicate findings, model performance metrics, and technical trade-offs to stakeholders through reports and presentations Stay current with the latest advances in deep learning, computer vision, and computational biology, and evaluate their applicability to internal research problems Education and Experience: Master's degree in Computer Science, Electrical Engineering, Bioinformatics, Computational Biology, or a related field with 5–7 years of relevant experience, or PhD with 0–3 years of experience Hands-on experience developing and deploying deep learning models for image analysis in production environments — either cloud-hosted or on-instrument — is required Strong proficiency with modern deep learning architectures including CNNs, Vision Transformers (ViT), U-Net, and attention-based models; familiarity with self-supervised or contrastive learning methods is a plus Experience with biological or biomedical image modalities (e.g., fluorescence microscopy, brightfield, high-content imaging) is strongly preferred Proficiency in Python and relevant deep learning and data science libraries: PyTorch, torchvision, OpenCV, Scikit-learn, NumPy, Pandas, and related tools Experience with cloud computing platforms (e.g., AWS), including model serving, containerization (Docker), and GPU-accelerated compute Familiarity with model calibration, uncertainty quantification, or performance evaluation frameworks is a plus Experience with single-cell or multiomic data analysis tools and workflows is a plus (not required) Knowledge of experimental design and statistical analysis Strong background in statistics and comfort reasoning about model outputs quantitatively Excellent problem-solving skills, attention to detai