nSight Surgical is the first machine learning platform that ensures hospital patient safety, and streamlines operative workflow. By recognizing patient safety moments in the operating room, the platform improves both patient safety, and increases efficiency across the entire hospital ecosystem.
As the MLOps Data Engineer, you will be responsible for designing and building the MLOps pipelines. You enjoy automating tasks and documenting your work to benefit others. From monitoring the complex ML models in production to improving best practices, prioritizing multiple issues, and troubleshooting complex problems, you’re ready to contribute to the architecture of a unified infrastructure system.
Our platform focuses on managing different components of the machine learning application development life cycle, starting from data ingestion, annotation, and exploration to model training, deployment, and monitoring.
Our engineers are self-motivated problem solvers who enjoy being versatile. We adore diversity and encourage healthy debate and discussion.
What We Need Your Help With:
- Design and build MLOps pipelines for data ingestion, selection, auto-ml, experimentation, optimization, continuous integration, deployment, verification, validation, and monitoring of ML models in production while following best practices of automation, monitoring, scale, and safety.
- Contribute to the architecture of a unified infrastructure system that bridges the gap between Data, ML, CI/CD, and Evaluation frameworks, improving the stability, security, efficiency, and scalability of the system.
- Evaluate the latest tools and frameworks in the ML ecosystem and help improve code quality through writing unit tests, automation, and performing code reviews.
- Identify patterns of data ingestion and pipeline issues and propose short and long-term solutions while working collaboratively with the computer vision team.
- Enable the CV teams to continuously experiment with new data and provide data and infrastructure support.
What We Look For:
- Masters or Ph.D. in Computer Science, Electrical Engineering, Statistics, or related technical field with at least 2+ years experience after graduation.
- Passion for automation by creating tools using Python.
- Expertise in MLOps infrastructure, machine learning model development, and deployment lifecycle.
- Experience with MLOps Frameworks like MLFlow, Kubeflow, DataRobot, Airflow, etc.
- Strong programming skills in Python or C/C++, with relevant experience in building AI applications using deep learning platforms like PyTorch or TensorFlow on GPUs
- Knowledge of basic statistical techniques (t-tests, confidence intervals, p-value, etc.).
- Experience building ML pipelines in a high-impact role, with years of AWS experience.
- Ability to design and implement cloud solutions, including building MLOps pipelines on cloud platforms such as AWS, MS Azure, or GCP.
- Knowledge of professional enterprise software development and practices, including software lifecycle, best coding practices, version control, architecture, testing, and deployment.
Nice to Have:
- Experience working with medical data.
- Understanding of healthcare or HIPAA-compliant systems.
- Experience with Docker, CI/CD build systems like Jenkins/Team City, and AWS services like S3, DynamoDB, EC2, ECR, lambda, etc.
- Proficiency in data visualization and dashboarding tools such as Superset, Elasticsearch and Kibana, Grafana, and Tableau
At nSight Surgical
nSight embraces diversity. We believe an inclusive and diverse workforce is an innovative one. We are an EEO employer and welcome all gender, race, culture, age, sexual orientation, and abilities to our team. If you require special accommodations throughout the process please reach out to firstname.lastname@example.org.