ABOUT ERODE AI

Erode AI is a climate-tech startup on a mission to revolutionize renewable energy forecasting and environmental monitoring with AI-driven models for predicting weather and weather-derived outcomes. We build scalable, user-friendly SaaS tools for energy traders, hydropower operators, and agricultural partners, leveraging cutting-edge AI technologies.
Live and work remotely, or at one of our offices in Calgary, Alberta or Charlottetown, PEI. Preference will be given to non-remote candidates.
Calgary, Alberta – Consistently ranked among the world’s most livable cities, Calgary combines big-city amenities with instant Rocky Mountain escapes. Enjoy Canada’s sunniest skies, an acclaimed culinary scene, and endless outdoor adventures—from mountain biking and hiking to world-class skiing—all just a short drive away.
Charlottetown, Prince Edward Island – Experience the charm of Canada’s smallest provincial capital, where historic brick streets meet red-sand beaches and lively culinary festivals. Savour farm-to-table dining, stroll scenic waterfront trails, and immerse yourself in a close-knit community that offers a vibrant arts scene and coastal adventures.

AT ERODE AI, WE BELIEVE THAT GREAT IDEAS COME FROM EVERYWHERE—AND WE’RE BUILDING A TEAM THAT REFLECTS A BROAD RANGE OF EXPERIENCES AND PERSPECTIVES.

We recognize that some individuals with non-traditional backgrounds may hesitate to apply unless they meet every qualification. But if you’re passionate about our mission and excited to grow with us, we strongly encourage you to apply — even if your experience doesn’t align perfectly with the job description.

ROLE SUMMARY

As an ML Research Engineer, you’ll help design, implement, and deploy novel deep learning models for forecasting weather and weather-derived outcomes like energy production and streamflow. You’ll stay at the cutting edge of AI research and be responsible for prototyping and productionizing new architectures, including adapting research papers into working code at scale.
This role sits at the intersection of applied machine learning and cloud-native engineering. You’ll contribute to everything from experimental model design and benchmarking to GPU-accelerated training workflows and deployment pipelines. Ideal candidates are fast-moving researchers who can write clean, efficient code, and are excited about real-world impact.

KEY RESPONSIBILITIES

• Prototype and train deep learning models using PyTorch or JAX, with architectures including CNNs, transformers, and diffusion models.
• Reproduce and extend methods from state-of-the-art research papers and adapt them to our forecasting tasks.
• Develop and scale training pipelines using GPU clusters, distributed training, and cloud-native tools.
• Optimize model inference for low latency and cost using techniques like quantization or LoRA.
• Collaborate with engineering and product teams to integrate models into production forecasting workflows.
• Contribute to internal model libraries, benchmarking experiments, and open-source initiatives where applicable.