Senior Machine Learning Engineer

HRB; Canada; Remote; CAD 90,000 - 130,000; Full time

Job summary

A leading startup focused on wildfire prevention is seeking a Machine Learning Engineer to develop innovative predictive models. This role offers the opportunity to work with state-of-the-art ML frameworks and contribute to groundbreaking technology aimed at reducing wildfire occurrences, using a variety of data sources, including real-time weather data and satellite imagery.

Qualifications

• 5+ years in machine learning, focus on environmental or geospatial applications.
• Expertise in ML frameworks and programming skills in Python.
• Experience with time-series analysis and computer vision.

Responsibilities

• Design, build, and deploy ML models for lightning prediction and wildfire risk assessment.
• Integrate large-scale data sources and ensure data consistency.
• Develop real-time data pipelines and alerting systems.

Skills

Machine Learning

Time-series Analysis

Computer Vision

Geospatial Data

Python

Tools

TensorFlow

PyTorch

JAX

AWS

Google Cloud

Spark

Dask

Job description

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 Engineer on this team, you’ll play a pivotal role in developing and deploying sophisticated models that predict lightning strikes and assess wildfire risk with unprecedented precision. Working with massive datasets and state-of-the-art ML frameworks, you’ll design scalable, high-performance models that drive proactive, preventive action. This position provides the unique chance to shape an innovative, mission-driven solution from the ground up, pushing the boundaries of what’s possible in environmental technology.

Key responsibilities

• Model development & optimization: Design, build, and deploy machine learning models for lightning prediction and wildfire risk assessment, focusing on time-series analysis, computer vision, and geospatial data

• Data integration & processing: Work with large-scale, diverse data sources—including real-time weather data, satellite imagery, and historical fire records—to support model development and ensure data consistency

• Architecture innovation: Experiment with and implement advanced model architectures (e.g., CNNs, transformers) to drive predictive accuracy and performance

• Real-time deployment & monitoring: Develop robust, real-time data pipelines and alerting systems to provide predictive insights for high-risk wildfire conditions

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

What we’re looking for

• Experience: 5+ years in machine learning, ideally with a focus on environmental or geospatial applications and experience deploying models in production

• Technical proficiency: Expertise in machine learning frameworks like TensorFlow, PyTorch, or JAX, with strong programming skills in Python

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

• Model development and optimization: Proven experience in model architecture design, hyperparameter tuning, and deploying optimized models in production environments

• Big data handling: Familiarity with distributed computing frameworks (e.g., Spark, Dask) and cloud platforms (e.g., AWS, Google Cloud) for scalable data processing and real-time model deployment

• Mission-driven mindset: A strong motivation to apply machine learning for environmental impact, with a passion for solving real-world challenges in a fast-paced, early-stage startup environment