Cloud ML Engineer

Job Description

We are seeking a highly autonomous Cloud ML Engineer / AI Infrastructure Engineer to architect and own our production environment. You will work directly with the CTO and Data Science team to bridge deep learning research and a scalable, cloud-native product, building the machinery that lets our models analyze massive datasets, specifically whole-slide images (WSIs), efficiently and reliably. We are looking for a builder who thrives with minimal supervision, takes full ownership of architectural decisions, and is excited by handling terabytes of complex medical data. This position begins remote and transitions to a hybrid model with periodic on-site collaboration.

Essential duties and responsibilities

• Infrastructure ownership: architect and maintain a secure, high-performance AWS infrastructure for heavy compute and massive data ingress and egress
• Production deployment: package and containerize trained DL models for high-availability inference, ensuring seamless transitions from research to production
• WSI pipeline management: build and optimize pipelines for processing and running inference on whole-slide images, managing the storage and memory constraints of high-resolution scanned data
• Strategic collaboration: work with the CTO and Data Science team to guide deployment feasibility and streamline the path to production
• Vendor and CI/CD integration: coordinate with cloud vendors for secure data transitions and manage GitLab-based CI/CD pipelines
• Custom reporting: develop automated reporting for processing results, model performance, and system health

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 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, with 3+ years in Cloud/ML engineering, preferably in a startup or fast-paced R&D environment
• Autonomy: proven ability to make high-level architectural decisions and execute with minimal supervision
• Problem solving: a track record of optimizing infrastructure for massive datasets (WSI experience a significant plus)
• Communication: ability to translate research requirements into production-ready infrastructure

Note: Send your resume, cover letter, and relevant work samples to careers@imagenomix.ai