Application Guide
How to Apply for Machine Learning Engineer
at Carbon Re
🏢 About Carbon Re
Carbon Re is unique as an AI startup specifically targeting the cement and steel industries, which are responsible for approximately 15% of global CO2 emissions. Their mission-driven approach combines cutting-edge machine learning with deep industry expertise to deliver tangible climate impact at gigaton scale. Working here offers the chance to apply ML skills to one of the world's most pressing environmental challenges while collaborating with domain experts in physics and chemistry.
About This Role
As a Machine Learning Engineer at Carbon Re, you'll be an individual contributor building, testing, and deploying models that directly reduce industrial emissions. You'll collaborate closely with product teams on customer projects while contributing to technical innovation across the full ML lifecycle. This role is impactful because your work will translate into measurable CO2 reductions in heavy industries where even small efficiency improvements have massive environmental consequences.
💡 A Day in the Life
A typical day might involve collaborating with physics experts to refine model inputs for cement kiln optimization, then implementing improvements in Python using PyTorch. You could be designing experiments to test new ML approaches for energy efficiency, reviewing deployment pipelines with the product team, and documenting best practices for the engineering team. The work blends technical ML implementation with cross-functional problem-solving focused on real-world emissions reduction.
🚀 Application Tools
🎯 Who Carbon Re Is Looking For
- Has 1+ years of hands-on ML engineering experience with both theoretical knowledge and practical implementation skills across multiple techniques
- Is proficient in Python and experienced with ML libraries like TensorFlow or PyTorch, with the ability to work in scientific computing environments
- Can collaborate effectively across disciplines, particularly with physics and chemistry experts, to solve complex industrial problems
- Demonstrates experience establishing best practices and improving internal processes in ML development workflows
📝 Tips for Applying to Carbon Re
Highlight specific experience with time-series forecasting or optimization models relevant to industrial processes in cement/steel production
Showcase projects where you've collaborated with domain experts (physicists, chemists, engineers) to solve scientific or industrial problems
Demonstrate your understanding of the full ML lifecycle by describing a project from data collection through deployment and monitoring
Include examples of how you've established or improved ML best practices in previous roles
Quantify your impact where possible - e.g., 'improved model accuracy by X% leading to Y reduction in computational resources'
✉️ What to Emphasize in Your Cover Letter
['Your specific experience with ML techniques applicable to industrial optimization and emissions reduction', 'Examples of successful cross-disciplinary collaboration, especially with scientific domains like physics or chemistry', "How you've contributed to establishing best practices and improving ML development processes", 'Your motivation to apply ML skills to climate technology and heavy industry decarbonization']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Carbon Re's specific approach to reducing emissions in cement and steel production (their website and publications)
- → The unique challenges of applying ML to heavy industry processes and emissions reduction
- → Current trends in industrial decarbonization and the role of AI/ML in climate tech
- → The company's recent projects or partnerships in the cement and steel industries
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on academic ML knowledge without demonstrating practical implementation experience
- Failing to show understanding of how ML applies to industrial or scientific domains
- Presenting generic ML experience without tailoring it to Carbon Re's specific mission and industry focus
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!