Senior Machine Learning Operations Engineer

We are  hiring a Senior MLOps engineer to join our rapidly growing Machine Learning team. As a member of the Machine Learning team, you will play a key role in the success of our team and company. You'll constantly be challenged to learn new technologies, establish best practices, and be given the freedom to solve problems on your own and learn by doing.

We are creating next-generation experiences across the newly 3D-digitized dental stack with ML models, so our ML platform is critical to our success. As a Senior MLOps Engineer, you will be key to the development of our ML platform to create various state-of-the-art machine learning models to revolutionize the digital dental industry.

What You'll Do

• In collaboration with ML engineers, design and implement MLOps pipelines for 2D & 3D dataset curation, model training, evaluation, optimization, and deployment.
• Manage and optimize cloud-based infrastructure for ML workloads, including scaling, resource allocation and cost management.
• Help engineer information feedback loops to continuously improve our machine learning models.
• Develop and implement automation strategies for model training, evaluation, optimization, and deployment to improve efficiencies.
• Develop and manage monitoring solutions using GCP tools like Cloud Monitoring and Cloud Logging to track model performance, system health, and operational metrics.
• Ensure that ML operations comply with data security and privacy regulations, utilizing security features and best practices.
• Collaborate with other stakeholders within Engineering and Data to maintain a high bar for quality in a fast-paced, iterative environment.

What We're Looking For

• 5+ years of software experience and 3+ years MLOps engineering experience, preferably in a high growth startup environment.
• 1+ years of experience working directly with machine learning model training and evaluation preferred.
• Hands-on experience working with ML models for performance and training optimizations, hyperparameter tuning, model monitoring, evaluation, and benchmarking.
• Hands-on experience with one of the cloud platforms such as AWS, GCP or Azure. Experience with Google Cloud services (e.g., Vertex AI, BigQuery, Dataflow, Compute Engine, Kubernetes Engine) is preferred.
• Familiarity with ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience building and maintaining CI/CD pipelines with best practices.
• Familiarity with containerization tools (e.g., Docker, Kubernetes) and orchestration platforms (e.g. Kubeflow)
• Comfort working in a highly agile, intensely iterative software development process.
• Self-motivated, self-managing and takes ownership, with excellent organizational skills.
• Ability to thrive in a remote-first organization.