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Dandy is building the operating system for dental offices around the world. As a Senior Software Engineer, you will build the foundation of our ML Platform, acting as the bridge between SoTA Computer Vision research and production-grade reliability. You will design and scale the infrastructure that handles massive 3D datasets, orchestrates complex training pipelines, and ensures our generative models are deployed with high reliability.
Job Responsibility:
Collaborate with Machine Learning Engineers to build the ML training pipelines that process massive 3D datasets, orchestrate model training, and enable continuous model improvements
Streamline the ML lifecycle, from data labeling and experimentation to deployment, by optimizing internal ML components and reducing technical debt
Develop and maintain cloud-native systems and tooling (GCP/Kubernetes) that support Dandy’s 3D dental products in a secure, well-tested, and high-performing manner
Write clean, maintainable code and tests that set the standard for our internal best practices
Partner with stakeholders across the Engineering organization to influence long-term architectural goals and maintain a high-quality bar
Requirements:
5+ years of experience as a Machine Learning Engineer or Software Engineer, ideally within a high-growth startup environment
Deep proficiency in building and operating ML platform components, including feature stores, model registries, distributed training infrastructure, and experiment tracking
Experience designing and running ML systems on cloud infrastructure, including containerization and orchestration technologies such as Docker and Kubernetes, and public cloud platforms (AWS or GCP or Azure)
Expertise in large-scale data processing, with proven experience building reliable ML data pipelines to support complex model training and evaluation
Experience creating and maintaining automated build, test, and deployment workflows across multiple environments (e.g., Buildkite, CI/CD pipelines)
Strong background in observability, including implementing metrics, logging, and tracing for complex, distributed production systems
Ability to communicate clearly and concisely about complex architectural problems and propose iterative, pragmatic solutions
Nice to have:
Experience with Python-based ML frameworks (e.g., PyTorch, TensorFlow)
experience with 3D geometric computer vision is a plus