We are looking for a friendly, motivated machine learning engineer who likes getting their hands dirty across the full stack, and is excited by the prospect of using cutting edge technologies to help restore our climate and natural environment - open to all experience levels from Junior to Senior.

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You’ll develop models, clean data, implement training pipelines, and manage production deployments to deliver state of the art vegetation segmentation and feature inference (on both local and cloud compute infrastructure) using a range of satellite, aerial imagery, and other remote sensing datasets. Together with the rest of the engineering, product, and marketing team you’ll work to understand our customers and their requirements, and use your machine learning expertise to deliver creative and innovative solutions to help them make the best use of their land for sequestering carbon, improving biodiversity, and driving climate resilience.

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You’ll join an experienced, friendly and collaborative team of existing ML and application engineers who care about each other and our mission. As an ML expert, you’ll drive best practice and continuous improvement across the group - we’re keen to learn from you too. While ML will be at the core of your role, you won’t be afraid to get stuck in and help wherever it’s needed. We’re a young company; we’re all here to work hard and support each other in making a difference, and to have fun doing it. This role is perfect for anyone with a growth mindset and a passion for being part of something positive.

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This position is based in Nelson, but we’re open to remote work for the right candidate.

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Responsibilities

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• Design, develop, deploy, and support the models and systems at the heart of CarbonCrop’s forest analysis and modelling stack.

• Collaborate with the engineering and product team to understand requirements, refine roadmaps and task definitions, and deliver solutions, while maintaining a high bar for quality in a fast-paced, iterative environment.

• Work outside the core ML stack as necessary, integrating the core technologies into the wider product in a way that meets the needs of the product and our customers.

• Advocate for, and implement, improvements to product quality, security, and performance.​

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Requirements

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• Proven experience as a Machine Learning Engineer or similar role, with significant experience in deep learning, including with at least one applicable framework, such as  Keras/TensorFlow or PyTorch.

• Experience training and serving computer vision models, preferably including experience with image segmentation and regression including the application of architectures such as U-net, ResNet, GAN and similar - where you’re not familiar with these you’ll be excited to learn.

• Experience sourcing, transforming, and cleaning both input and label data.

• An ability to write robust, testable, maintainable code in Python, and to provide constructive feedback on the code of others.

• Excellent communication and writing skills, with the ability to clearly document procedures, processes and work performed.

• Excellent organisational skills - you’ll thrive in a startup environment and be comfortable juggling priorities and responding to change with grace and flexibility.

• Enthusiasm for working collaboratively in a team and supporting others.

• A ‘do anything’ attitude - we are small, and the problem and opportunity we face are big. You don’t let your role define you, and will pitch in anywhere needed.

• Degree in computer science, engineering, or physical sciences.

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Bonus points

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• Experience working with remote sensing data and especially satellite imagery at scale.

• Experience processing LiDAR / Synthetic Aperture Radar data.

• Experience in the application of unsupervised/self-supervised techniques, especially to landscape and vegetation analysis.

• Masters or PhD in computer science or related discipline.

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Our stack

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• Python (Keras/Tensorflow, FastAPI)

• React

• Postgres

• AWS

• MLFlow

• Your favourite frameworks, tools, approaches… we’re eager to experiment and learn from what works elsewhere

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How to apply

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This position has now been filled.

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If you would like to hear about future Machine Learning vacancies at CarbonCrop, please email us at: careers@carboncrop.nz