Manager, ML Platform and Infrastructure

Dark Vision is seeking a hands‑on Engineering Manager to lead our ML Platform and Infrastructure team. You will be responsible for the strategic planning, architecture, and reliability of the compute and data systems that power our advanced acoustic imaging technology. Dark Vision's ultrasound imaging system creates the largest ultrasound datasets in the world, with single scans approaching petabytes in size. Detecting millimeter‑sized defects across hundreds of kilometers of assets requires a platform that is robust, scalable, and efficient.

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.

You will lead the ML Platform and Infrastructure team, a specialized unit within the broader Imaging & AI group. Your direct reports include Cloud Infrastructure Engineers, MLOps Engineers, and Data Scientists. You will work closely with the Applied ML team to ensure their algorithms have the compute resources and clean data required to move from experimentation to production.

• Team Leadership & Strategy:
  Manage, mentor, and grow a diverse team of engineers and scientists. You will set the technical roadmap for our ML infrastructure, balancing immediate production needs with long‑term scalability goals.
• Platform Architecture:
  Architect the cloud‑based (AWS) and on‑premise systems required for massive‑scale batch processing. You will make high‑level decisions regarding compute orchestration (Kubernetes), storage, and cost optimization.
• Operational Excellence:
  Oversee the development of CI/CD pipelines and MLOps practices. You will ensure our training and inference workflows are reproducible, monitored, and secure.
• Data Integrity & Validation:
  Supervise the Data Science function to ensure the statistical validity of our models. You will champion data integrity audits and experimental design frameworks that prove the reliability of our technology to clients.
• Cross‑Functional
  
  Collaboration:
  
  Serve as the bridge between Software Engineering, Applied ML, and Data Analysis teams, ensuring that infrastructure decisions support the company’s product delivery goals.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in software, infrastructure, or data engineering.
• 2+ years of experience leading or managing technical teams.
• Deep technical understanding of cloud infrastructure (AWS) and container orchestration (Kubernetes).
• Proficiency in Python and familiarity with the machine learning lifecycle.

• Experience managing multidisciplinary teams (combining Dev Ops/Infra with Data Science).
• Hands‑on experience with workflow orchestration tools (e.g., Prefect, Dagster, Airflow).
• Experience processing petabyte‑scale datasets.
• Familiarity with distributed computing frameworks (e.g., Ray, Dask).
• Experience dealing with data governance, security, and compliance in an industrial context.
• Pragmatic leadership style with the ability to discern when to build custom solutions versus utilizing managed services.
• Exceptional communication skills to articulate complex infrastructure constraints to non‑technical stakeholders.

For this role, we anticipate paying $150,000 to $200,000 per year. This role is eligible for variable pay, issued as a monetary bonus or in another form.

Dark Vision Technologies Inc. is a Canada‑based tech company disrupting the industrial imaging market since 2013. We have created the world’s most advanced acoustic‑based imaging platform. We are packaging it into multiple new product lines, revolutionizing how our clients quantify and visualize the integrity of their critical assets.

Backed by Koch Inc., one of the world’s largest privately held companies, Dark Vision’s team of Mechanical, Skunk works, Electrical, Software, and Machine Learning Engineers is rapidly expanding to meet the demand for the company’s current and upcoming products.

We allow employees to work on cutting‑edge technologies that blend science with real‑world…