Machine Learning Scientist

Your Job

Dark Vision is seeking a Machine Learning Scientist to join our Imaging & AI team. You will research, design, and prototype the deep learning architectures that power our automated analysis tools. You will solve complex computer vision challenges to enable interpretation of industrial data at scale.

Dark Vision’s ultrasound imaging system collects huge datasets on the order of terabytes, detecting sub‑millimetric defects in industrial assets that span hundreds of kilometers. It is essential that we develop algorithms for the automated processing, classification, and visualization of this data. As a Machine Learning Scientist, you will focus on the mathematical and architectural challenges of interpreting this data, and creating models that learn efficient and generalizable representations for real‑world industrial applications.

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!

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 other technical members to ensure our models are integrated seamlessly into our products.

• Applied Research & Architecture Design: Research and develop state‑of‑the‑art deep learning architectures to solve large‑scale industrial problems. You will target tasks such as object detection, instance segmentation, semantic segmentation, and anomaly detection.
• Model Prototyping: Rapidly prototype and validate new model concepts using Python and deep learning frameworks. You will own the experimental phase, ensuring models are statistically valid and effective before collaboration with engineering.
• Data Strategy: Work closely with Data Analysts and Data Scientists to explore and organize datasets. You will define the data extraction and preprocessing strategies required to train robust models.
• Performance Optimization: Continuously monitor model success metrics and research improvements to increase model robustness and accuracy on challenging, real‑world data.
• Documentation & Knowledge Transfer: Document model architectures, training details, and dataset procedures to ensure reproducibility, and facilitate smooth integration by the engineering team.

• Master’s degree in Computer Science, Electrical Engineering, or a related field;
  PhD preferred
• 3+ years of experience developing or researching deep learning models
• Proficiency in Python and deep learning frameworks such as PyTorch, JAX, etc.
• Ability to design experiments and interpret complex results

• Experience processing medical or industrial ultrasound images
• Publications in top‑tier computer vision venues (e.g., CVPR, ICCV)
• Experience with self‑supervised or unsupervised learning methodologies
• Hands‑on experience with ML lifecycle management tools like MLFlow, Amazon Sage Maker, GPC Vertex AI, etc.
• Hands‑on experience with orchestration tools like Kubeflow, Prefect, Airflow, etc.
• Experience leading or mentoring other scientists or researchers
• Ability to work in a fast‑paced, self‑driven environment
• Excellent communication skills to present research findings and write technical reports

For 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.

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  webpage.

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

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