Software Engineer (ML) (Mid-Level / Senior)

Apply your engineering skills to combat global warming and become part of the global movement for climate action.

About Seqana :

Seqana is a start-up building digital monitoring, reporting, and verification (MRV) tools using remote sensing and AI to drive soil carbon sequestration and support the transition towards regenerative, climate-preserving agriculture at scale. We are backed by venture capital investors and supported by public funding from ESA and DLR.

Capturing carbon in soils through regenerative agricultural practices is a powerful and still underutilized solution to climate change, with significant co-benefits including improved soil health, water quality, food security, and biodiversity.

Existing MRV solutions for soil carbon are a major driver of cost and complexity in carbon projects and often limit scalability. At Seqana, we combine satellite imagery, an extensive proprietary library of soil datasets, and machine learning to deliver an accurate, cost-effective, and scalable MRV solution for soil organic carbon. Our product enables project developers, agrifood corporations, NGOs, and government entities to credibly measure, quantify, and report the climate impact of regenerative agricultural practices.

The Role :

Our growing team is looking for an experienced and motivated software engineer (mid-level or senior) to help us scale our technical infrastructure and analytical products for soil carbon quantification. This role builds on and expands our ML and geospatial systems, taking ownership of core ML workflows and pipelines, while also supporting the evolution of our backend and cloud infrastructure. You will be responsible for building robust and scalable systems that power our MRV tools, helping us deliver trustworthy and impactful climate solutions.

We're looking for someone with strong software engineering skills, a background in ML- and data-intensive systems, and an interest in geospatial or environmental data. You will join a cross-functional, mission-driven team working to bring transparency and integrity to climate action. We value proactivity, authenticity, empathy and solution mindset in our company culture.

Preferred start date : As soon as possible

Vacation days : 30 days PTO

Perk : 500,00 € training budget to use for your professional self-improvment, flexible and hybrid work environment

Type of Employment : Full-time, unlimited contract

Tasks

• Own and improve our ML training and evaluation pipelines, making them more reliable, flexible, and easier to extend
• Improve configuration, maintainability, and traceability of our ML workflows (e.g., parameter handling, experiment tracking, pipeline design)
• Reduce complexity and improve reliability in our Google Earth Engine (GEE) feature pipeline
• Contribute to the development of our geospatial data infrastructure, extending our capacity to store and process vector files, as well as high-resolution raster datasets
• Own and improve our CI / CD and testing strategy, supporting integration tests, dependency upgrades, and stronger release hygiene
• Support with further development of our GCP infrastructure, adapting our architecture, monitoring and access management to evolving needs

Requirements

What makes you stand out :

Benefits

Join us if you want to advance climate action in your daily work and are open to new insights and opportunities everyday. Please, email us with your CV and a short motivational letter via JOIN.

Seqana is committed to building a workplace culture based on strong values, where equal employment opportunity is guaranteed to all applicants and team members regardless of their race, color, ethnicity, sex, sexual identity, religion, or disability.

Seqana is a environmental tech startup that specialises in monitoring soil carbon sequestration based on satellite imagery and a novel machine-learning algorithm.

We offer our customers a fast, credible and cost-efficient way to monitor agricultural carbon sequestration. Our unique approach combines satellite data, ML modelling, and superior soil datasets. The result is a scalable and low-cost technology to quantify soil organic carbon changes over time (SOC).

As a result of our technology, our customers are able to save costs, increase measurement accuracies, and facilitate data driven decision making.