Senior ML Engineer- Dendra Systems Remote £81,000 – £100,000 a year – Full-time Dendra Systems Remote £81,000 – £100,000 a year

Full time @Data Science Career in Machine Learning
  • Apply Before : July 19, 2024
  • Salary: £81.00 - £100.00 / Annual
  • 1 Application(s)
  • View(s) 40
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Job Detail

  • Job ID 23293
  • Sector Machine Learning

Job Description

Full job description

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 analyse 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 the implementation of cutting-edge computer vision techniques (object detection, classification, and semantic segmentation), you will have applied these techniques commercially within a small agile cross-functional team and have demonstrable experience of fusing machine learning and traditional data science approaches to achieve customer value. It will be essential to be proficient at deep learning by keeping abreast of and building upon the latest research. Additionally, you will need to leverage your understanding of how our ecologists operate to customise these methodologies for optimal outcomes. The ideal candidate should also capable of effectively communicating with non-technical audiences. We value individuals with very broad and diverse skills.

Key Accountabilities

  • Designing and implementing ecological products based on remote sensing, i.e. tree counting, canopy size detecting, or area classification of certain species. The role is very cross-domain and will involve working closely with Product, Ecology and Engineering specialists to build flexible solutions that bridge ecological requirements and business needs to select the right approaches.
  • Experience in developing practical deep learning applications for computer vision
  • Solid theoretical understanding of machine learning and neural networks
  • Solid hands-on software development skills in Python (numpy, pandas, scipy, scikit-learn)
  • Ability to write clear, efficient and scalable code
  • Experience with one or more Deep Learning Frameworks (PyTorch, JAX, Keras, Tensorflow)

Requirements

  • Proficiency with Python
  • Proficiency with git
  • Proficiency with Linux use and admin
  • Experience deploying cloud services (bonus for AWS experience)
  • Machine learning background, especially with PyTorch for computer vision
  • Appreciation of code quality and the value of a well tested code base
  • Problem-solving aptitude
  • Creative thinking skills

Benefits

  • Work From Home, with budget for home working setup
  • Choose your own computer
  • Employee Assistance Programme
  • Stock Option Plan
  • 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

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