Software Engineer, Sylvera
Field Data Science
+ Equity
1-2 days a week in office
Software underpinning carbon markets
Job no longer available
Software underpinning carbon markets
101-200 employees
Job no longer available
+ Equity
1-2 days a week in office
101-200 employees
Company mission
To make carbon offsets work for everyone.
Role
Who you are
- Has demonstrable experience and skills in developing production pipelines in Python with software engineering practices including Git, CD/CI and test driven development (desirably with experience using Docker, CircleCI and Dagster, or similar)
- Has a keen interest and desire to work with machine learning (experience with PyTorch and computer vision models such as CNNs desirable)
- Ideally has previous experience working with geospatial data (GDAL and xarray desirable)
- Cares deeply about the climate and ecosystems of the earth
- Is a self-starter who thrives in constantly evolving environments, ideally with early-stage experience
What the job involves
- The Field Data Science team at Sylvera is on a mission to accurately value the carbon stored across the world’s ecosystems, so that our customers can be sure that they are investing in real climate action
- We do this by realising the combined potential of spaceborne remote sensing and AI for ongoing and accurate carbon accounting; something that has to date been limited by the lack of quality in-situ training data
- Our team collects and processes these data - we have for example, pioneered multi-scale lidar methods for above-ground biomass estimation, that sees us collect laser scanning measurements from across the world’s forests using drones and helicopters
- The team are currently working on building the capability to map soil organic carbon (SOC) stocks globally using machine learning and Earth observation data
- Developing this pipeline with good software engineering practices front of mind, such that the resulting data can be reliably and scalably consumed by internal and external customers
- Working in collaboration with scientific and engineering experts to design, construct and test machine learning models that use various geospatial covariates to predict soil organic carbon
- Proactively collaborating with colleagues across Sylvera; including with our Earth Data Platform team to collaborate on scalable engineering and ML practices, and with the customers of the resulting data
- In case you’re interested, our tech stack is as follows (not necessary for you to have experience in all of these):
- Python – key packages:
- Testing: pytest
- Geospatial: Xarray, Rioxarray, GeoPandas, GDAL
- ML: PyTorch, SKLearn
- NumPy
- Git & GitHub
- Docker
- CD/CI: CircleCI
- Data pipeline orchestration: Dagster
- Cloud data platform: Arraylake, Snowflake
Salary benchmarks
Our take
Sylvera is scaling carbon markets with software, by developing tools to quantify stocks of carbon stored in vegetation using AI applied to satellite or drone data.
Using current quantification techniques, it is not possible to provide accurate and regular data on the performance of forestry projects, which leads to issues with corruption, fraud and uncertainty. This leads to significant financial and reputational risk for corporates purchasing offsets and makes it hard for high quality forest projects to secure fair prices and low cost finance at large scale.
By providing a quantitative sequestration audit trail and associated risk ratings, Sylvera intends to underpin effective voluntary carbon markets. Its 2023 funding round is being put towards its entrance into the US market and hires across engineering and product.
Steph
Company Specialist at Welcome to the Jungle
Insights
279% employee growth in 12 months
Company
Company benefits
- Unlimited Holiday
- Christmas Shut Down
- Private Medical & Life insurance
- Enhanced maternity/paternity leave
- Monthly Friday lunch budget
- Team social budgets
- One-off WFH allowance
- Enhanced Paid Sick Leave
- Mental Health support
Company HQ
London, UK
Leadership
Allister Furey
(CEO & Co-Founder)Allister is a repeat entrepreneur with a PhD in Machine Learning and an MBA from London Business School. As well as 10 years developing technology in the renewables industry, he has built and operated business units in Asia, Continental Europe and the UK
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