ML Operations Engineer
Dendra Systems provides tools and solutions for restoring ecosystems around the world. Our focus is on promoting biodiversity and restoring natural habitats for the benefit of landowners, businesses, governments, and communities. Our goal is to address climate change through natural solutions using cutting-edge technology.
We are a team of innovative builders who are committed to restoring ecosystems on a global scale. Our advanced AI/ML technology helps us analyze various types of data, such as remote sensing satellite data, aerial survey data, and drone survey data, to assess land conditions and determine the potential for carbon sequestration and biodiversity. Our knowledge management platform enables our customers to take informed actions, including drone swarm seeding, invasive species and erosion management, to grow and monitor the ecosystems needed to restore the natural world we all rely on.
We are looking for passionate people who are interested in solving problems to restore the natural world at-scale. Every day there will be plenty of opportunity to influence the direction of our product and the cultural and technical direction of the team. You will build software that runs at scale, you will learn and experiment with the latest technologies to innovate, you will help other engineers on the team grow to their full potential.
To succeed in this role, you should be experienced in automation and improving the scalability, reliability and efficiency of production ML processes, and improve the quality and cycle time of our analytics. You will understand the importance of putting the delivery of customer value first and have the skills to take an idea, shape it, implement it, deliver it and maintain it to the highest standards.
- Productionisation of machine learning models
- Developing and maintaining infrastructure to support ML model development, training and inference
- Automate data flows and reporting pipelines
- Improving data lifecycles: create automated diagnostic reports for data quality, data drift, cycle times
- Assisting the machine learning scientists with their workflows
- Tracking datasets and model artefact lineage and ensuring reproducibility
- Collaborate as part of the development process to help shape and refine the scope of ideas
- Continually look for opportunities to improve operational excellence
- Proficiency with Python
- Proficiency with git
- Proficiency with Linux use and admin
- Experience orchestrating tasks using frameworks such as AirFlow
- Experience deploying cloud services (bonus for AWS experience)
- Experience scaling workloads using distributed computing frameworks (especially PySpark, Ray Distributed)
- Machine learning background, especially with PyTorch
- Experience with containerised processes (Docker and Kubernetes)
- Appreciation of code quality and the value of a well tested code base
- Problem-solving aptitude
- Creative thinking skills
- Remote working
- Flexible hours
- Competitive salary
- 25 day per year annual leave
- Financial support towards optical, chiropody, dental and therapy treatments as well as 24 hour advice and information line