MLOps Engineer

Your Job

DarkVision is seeking an MLOps Engineer to join our Imaging & AI team. You will design, build, and maintain the automation platforms that power our machine learning lifecycle. You will be the force multiplier that enables our researchers and engineers to ship models faster and more reliably.

DarkVision’s ultrasound imaging system collects huge datasets on the order of terabytes, detecting sub-millimetric defects in industrial assets that span hundreds of kilometers. Managing the flow of this data requires robust automation. You will focus on "productionalizing" the machine learning process—building the CI/CD pipelines, training infrastructure, and deployment systems that ensure our models perform consistently in the real world.

This role is on-site at our North Vancouver, BC HQ, where employees enjoy a wide array of amenities including a fully equipped gym, squash court, steam room, climbing wall, and more!

Our Team

Working in the Imaging & AI team, you will join a multidisciplinary group of scientists and engineers. This team is responsible for early-stage ideation, research, experimentation, and development. You will collaborate closely with Machine Learning Scientists and Cloud Infrastructure Engineers to bridge the gap between experimental code and production systems.

What You Will Do

• Build ML Platforms: Design and maintain the cloud-based infrastructure (AWS) that supports scalable model training and batch inference pipelines.
• Automate the Lifecycle: Develop and manage CI/CD pipelines for machine learning, ensuring that model training, testing, and deployment are automated and reproducible.
• Model Operations: Implement best practices for model versioning, registry management, and artifact tracking. You will ensure that every model in production is traceable and secure.
• Reliability & Monitoring: Proactively manage system health by implementing monitoring and logging tools. You will lead incident responses for the ML stack and ensure high availability for data processing workflows.

Who You Are (Basic Qualifications)

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 3+ years of professional experience in a DevOps, MLOps, or Software Engineering role.
• Proficiency in Python and experience with automation scripting.
• Experience with cloud infrastructure, specifically AWS (SageMaker, Batch, Lambda, S3).
• Proficiency with containerization and orchestration tools (Docker, Kubernetes).
• Experience building CI/CD pipelines for software or machine learning projects.

What Will Put You Ahead

• Experience with workflow orchestration tools (e.g., Prefect, Airflow, Kubeflow).
• Experience with Infrastructure as Code (IaC) tools like Terraform and Ansible.
• Familiarity with MLOps tools for experiment tracking (e.g., Weights & Biases, DVC, MLFlow).
• Knowledge of model optimization techniques (quantization, pruning) for deployment.
• Understanding of security best practices in a cloud environment.
• Strong communication skills and ability to work in a team.

General Salary RangeFor this role, we anticipate paying $100,000 to $150,000 per year. This role is eligible for variable pay, issued as a monetary bonus or in another form.

Additionally, everyone has individual work and personal needs. We seek to enable the best work environment that helps you and the business work together to produce superior results.

How to Apply

If you have the above qualifications, we would like to hear from you. We thank all applicants in advance, but please be advised that only those selected for an interview will be contacted.

We are an equal opportunity employer. If you require accommodation or assistance at any time during the application or selection processes, please submit a request by following the directions located in the FAQ section at the bottom of the kochcareers.com webpage.

Successful candidates will be required to complete a criminal background check.

Keywords: MLOps, DevOps, AWS, CI/CD, Kubernetes, Python, Model Deployment, Infrastructure, Automation, SageMaker, Docker.

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