Senior Engineer, Machine Learning

Essential Functions And 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

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)

Physical Requirements

• Frequently moves boxes weighing up to 20 pounds

Job Type

• Full-time/Exempt

Base Compensation

• Pay Range $139,000 - $183,000

Additional Benefits

• Stock options
• Discretionary annual bonus
• No cost health insurance plans
• 401k with company match
• Flexible paid time off