We are seeking a talented engineer to join our Soil Carbon team as a Machine Learning Engineer . The successful candidate will contribute to the revolutionary solution as part of the world-renowned Soil Carbon program - aimed to bring together farmers and clean technology to turn climate change, economic crisis, and food insecurity into opportunities.
To support our work in soil carbon analysis using deep learning techniques on large spectral datasets sourced from satellites, drones, field rovers, and lab measurements.
Reporting to the Machine Learning Manager, the Machine Learning Engineer will work closely with our Robotics and Software Engineers to help process satellite, drone, and field rover images from massive multispectral datasets and use them to gain insight into soil properties.
Research and help develop machine learning / deep learning models for soil carbon quantification and recommendation
Support implementation of analysis pipelines to process large hyperspectral datasets captured by imaging platforms
Research and help implement algorithms involving active learning, uncertainty modeling, and semi-supervised learning to improve sampling and model performance
Collaborate with and receive guidance from our Software and Machine Learning Engineers to put your models and algorithms into production
M.Sc. or Ph.D. in Engineering / Computer Science, or an equivalent combination of experience and knowledge
2+ years’ experience in applying machine learning and deep learning concepts to real-world problems
Some experience professionally applying machine learning techniques in at least one of the following domains : remote sensing, satellite imaging, soil science, soil carbon, and / or spectroscopy
Familiarity with deep learning network architectures (e.g., convolution neural networks, recurrent neural networks, single shot detectors) and frameworks (e.
g. Tensorflow, PyTorch, Caffe)
Solid programming skills with a focus on writing clean / maintainable code with experience in Python (preferred), Java, or C++ programming
Strong analytical ability and mathematical skills
Matured communication and critical thinking ability to influence and propose analytics strategies that challenge status quo thinking
A background in soil science or agriculture
Portfolio of completed machine learning projects available for review
Experience with parallel and distributed training of deep learning models
Experience applying machine learning techniques to massive datasets
Exposure to imaging system components (sensors, optics, lighting)
Experience with Bayesian neural networks and / or active learning
Exposure to GIS processing tools (e.g., ArcGIS, Geopandas)
Knowledge of the Agile project management methodology
QUALITIES WE’RE LOOKING FOR
Aptitude for interdisciplinary collaboration
Highly conscientious with strong follow-through
Capable of performing research on best practices and communicating results to a non-expert audience
Able to apply domain knowledge to ambiguous and novel situations
This position is remote based in Canada or United States