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.

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.
Experience with Python-based ML frameworks (e.g., PyTorch, TensorFlow); experience with 3D geometric computer vision is a plus

Senior Software Engineer – ML Platform

Key skills