Machine Learning Engineer (Intern)

What is Sylvera anyway? ‍👩‍👨🌳

Sylvera provides carbon data for genuine climate impact. 

Purchasing credits through the carbon markets is one of the most established and scalable ways to channel finance from the private sector to effective climate solutions and work toward societal net zero. Unfortunately, the voluntary carbon markets have been plagued with mistrust and a lack of effectiveness since they’ve emerged – until Sylvera. 

We produce the most accurate and trusted data about the carbon markets so that businesses and governments can confidently invest in, deliver, and report on real climate impact. Some of the world’s largest corporations, consultancies, and financial services institutions rely on Sylvera for their climate investment decisions. 

Our team is made up of the leading minds in climate change from scientists to academics. We work in partnership with scientific organisations, universities, governments and think tanks to develop and test rigorous and holistic ratings methodologies, leveraging the latest technology. Founded in 2020, Sylvera has 150+ employees across the world with offices in London and Belgrade. We’ve raised over $35m from leading VCs like Index Ventures and Insight Partners to date. 

What will I be doing? ‍‍👩‍💻👨‍💻

Having found early product-market fit in a nascent, rapidly growing space, we’re now scaling our Machine Learning team at pace. We’re looking for a mission-driven engineer to join our cross-functional machine learning team. Specific Responsibilities will include:

- Working on high-impact projects and innovate new solutions to problems in the earth observation space and machine learning

- Working closely with machine learning and remote sensing engineers to process data from various satellite sources

- Developing cutting-edge machine learning classifiers, models and algorithms operating on remote sensing data

We’re looking for someone who: 🧠💚

- Has a BSc, MSc or PhD in Computer Science, Remote Sensing, Maths, Physics or a related technical field or relevant work experience

- Has experience writing modular code in Python (through internships, work experience or personal projects)

- Possesses familiarity with Satellite data, aerial data or other geospatial data products

- Cares about the climate and ecosystems of Earth, and to want to change incentives so they get valued and protected.

Nice to have:

- Experience solving problems with Machine Learning using Deep Learning tools such as Tensorflow or PyTorch

- Exposure to Google Earth Engine, aerial images

- Exposure to SAR or LIDAR dataPreference for candidates who can transition to a full time role after the internship.

We’d like someone highly ambitious, motivated and eager to propel their career forward. We prioritise grit, positivity, and the willingness to get stuck in, and encourage you to apply even if your experience doesn't exactly match this job description

Benefits 💰

- Equity in a rapidly growing startup

- No corners cut in having the best tech to do your job

- Unlimited annual leave - and encouragement to actually use it!

- Half Day Fridays - 2pm finish every Friday

- Enhanced parental leave 

- Up to 20 days paid sick leave 

- £500 WFH allowance 

- Access to Mental Health support via Spill

- Monthly team socials

Location 🌍 

UK (with access to an office in London if you wanted to come in) 

Our Values 🙌

Own it: We believe in giving people the autonomy to own their impact.

Stay curious: Our business is built on transparency and debunking misperceptions.

Do what’s right - even when it’s hard: The right path isn’t always easy or obvious.

Collaborate with kindness: Great teams are built on open communication and respect.

What if you’re a partial fit? 🌱

We prioritise grit, positivity, and the willingness to get stuck in, and encourage you to apply even if your experience doesn't exactly match this job description. 

Equal employment opportunity 🌈

Sylvera is an equal opportunity employer: we value diversity. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.