Cloud ML Engineer

Our portfolio company Imagenomix (www.imagenomix.ai), is an AI-driven digital pathology startup that has spun out from NYU. The company recently raised $3.2M from Modi Ventures, Verge HealthTech Fund, Metavallon VC (Brain Gain Fund) and others.

Imagenomix is seeking a highly autonomous Cloud ML Engineer / AI Infrastructure Engineer to architect and own the company's production environment. You will work directly with the CTO and Data Science team to bridge the gap between deep learning research and a scalable, cloud-native product.

This role is centered on building the "machinery" that allows models to analyze massive datasets - specifically Whole Slide Images (WSIs)- efficiently and reliably. The company is looking for a builder who thrives with minimal supervision, takes full ownership of architectural decisions, and is excited by the challenge of handling terabytes of complex medical data. This position begins as remote and will transition to a hybrid model with periodic on-site collaboration.

Essential Duties and Responsibilities

• Infrastructure Ownership: Architect and maintain a secure, high-performance AWS infrastructure designed for heavy compute tasks and massive data ingress/egress.
• Production Deployment: Package and containerize trained DL models for high-availability inference, ensuring seamless transitions from the Data Science team’s research to production.
• WSI Pipeline Management: Build and optimize pipelines for processing and running inference on Whole Slide Images, managing the unique storage and memory constraints associated with high-resolution scanned medical data.
• Strategic Collaboration: Work closely with the CTO and Data Science team to guide deployment feasibility, proactively identify infrastructure bottlenecks, and streamline the path to production.
• Vendor & CI/CD Integration: Coordinate with cloud vendors for secure data transitions and manage GitLab-based CI/CD pipelines to ensure rapid, reliable deployment cycles.
• Custom Reporting: Develop automated reporting tools to summarize processing results, model performance metrics, and system health for both technical and leadership stakeholders.

Required Technical Stack

• Cloud: Advanced AWS expertise (EC2, S3, ECS/EKS, Lambda, SageMaker, IAM).
• ML Frameworks: Deep proficiency with TensorFlow and PyTorch, and expertise in Python.
• DevOps/VCS: Expert knowledge of Git for version control and CI/CD orchestration.
• Containerization: Mastery of Docker and Kubernetes (EKS).
• Infrastructure as Code: Experience with Terraform or CloudFormation.
• Data Science Ecosystem: Proficiency with CV and ML libraries (OpenCV, NumPy, Pandas, Scikit-learn).

Qualifications

• Experience: PhD or MS in Computer Science, Data Science, or a related field. 3+ years in Cloud/ML Engineering, preferably within a startup or a fast-paced R&D environment.
• Autonomy: Proven ability to make high-level architectural decisions and execute them with minimal supervision.
• Problem Solving: A track record of optimizing infrastructure for massive datasets (WSI experience is a significant plus).
• Communication: Ability to work closely with cross-functional teams to translate research requirements into production-ready infrastructure.

Physical Demands & Work Environment

• Prolonged periods of sitting at a desk and working on a computer.
• Specific visual abilities required by this job include close vision and the ability to adjust focus.
• Work is performed in a split environment; initially a remote home-office setting, transitioning to a standard indoor office environment with moderate noise levels.

Equal Opportunity & Accommodations

• Imagenomix is an Equal Opportunity Employer. They celebrate diversity and are committed to creating an inclusive environment for all employees.