At MetEn, we believe in combining passion, expertise, and collaboration to drive the energy transition forward. Our team is diverse and driven, supporting each other as we tackle industry challenges and create impact. Work with cutting-edge technology, learn from industry leaders, and make a real difference in sustainable energy.”
“Working here is not just a job—it’s a chance to impact the world’s transition to cleaner energy.”
– Faysal, Data Scientist
MetEn aims to accelerate the shift to renewable energy by providing state-of-the-art predictive and optimization tools. We empower energy operators with data-driven insights to reduce costs and maximize efficiency
Innovation: We strive to push boundaries in renewable energy technology.
Integrity: Transparency and honesty are at the heart of what we do.
Sustainability: We are committed to leaving a positive impact on the environment and the communities we serve.
We value diversity as a source of strength. Our inclusive environment encourages diverse perspectives and collaboration, promoting equality in opportunity and representation.
We are committed to fair hiring practices, equal pay for equal work, and creating pathways for underrepresented groups in technology and renewable energy.
Yes. Many of our roles offer flexible working arrangements, including remote and hybrid options, depending on the position and project requirements.
After applying, our team reviews your application. Selected candidates are invited to one or more interview stages, which may include technical discussions or case-based assessments.
We value strong foundations in software development, data analysis, machine learning, or engineering. Experience in energy systems, AI, or optimization is a plus, but motivation and problem-solving skills are equally important.
Not necessarily. While experience in energy or sustainability is beneficial, we also welcome candidates with strong technical skills who are eager to apply them in the energy domain.
Yes. We encourage continuous learning through challenging projects, collaboration with experienced team members, and exposure to real-world problems in AI and energy systems.
