Job Description

Remote - Canada

Your opportunity

Founded in 2024 our client is an early-stage startup with a pioneering approach to wildfire prevention, leveraging novel, predictive models to prevent catastrophic wildfires ignited by lightning over (and near) high-risk areas.

Lightning strikes account for 60% of wildfires in Canada, resulting in 93% of the burned area and emissions—their technology focuses on reducing wildfire occurrences and emissions by suppressing lightning strikes before they ignite these fires.

Their work combines cutting-edge geospatial data analysis, machine learning, and computer vision to create a first-of-its-kind solution that anticipates and prevents lightning-induced wildfires at their source. This is a rare opportunity to build entirely novel capability and to contribute to a critical area of research that’s largely uncharted.

As a Machine Learning Operations (ML Ops) Engineer, you will play a critical role in bridging the gap between research and scalable production systems.

Amongst a range of responsibilities, you can expect:

• Collaboration & Innovation : Partner closely with data scientists, machine learning engineers, and domain experts to integrate models into an IRL ecosystem

• Model deployment: Transition experimental machine learning models into robust, production-ready services with containerization tools and orchestration platforms to ensure reliable model serving in a dynamic environment

• Pipeline automation: Work with large-scale, diverse data sources (real-time weather data, satellite imagery, and historical fire records) to support model development and ensure data consistency

• Monitoring & maintenance: Implement comprehensive monitoring solutions to track model performance, detect data drift, and trigger retraining as needed

• Infrastructure management: Optimize resource allocation to balance performance with cost efficiency

• Collaboration & cross-functional work: Partner with data scientists, software engineers, and environmental scientists to integrate models and translate findings into actionable strategies

What we’re looking for

• 5+ YOE in ML, ideally with a focus on environmental or geospatial applications and experience deploying models in production

• Motivation to apply machine learning for environmental impact, with a passion for solving real-world challenges in a fast-paced, early-stage startup environment

• Expertise in machine learning frameworks like TensorFlow, PyTorch, or JAX

• Experience with time-series analysis and computer vision techniques, particularly for satellite imagery, object detection, and segmentation

• Experience in model architecture design, hyperparameter tuning, and deployment

• Familiarity with distributed computing frameworks, cloud platforms and real-time model deployment

It’s a bonus if

• You have startup/founding experience

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