About the Role
The role involves leading the development of machine learning models, integrating them into production systems, and working cross-functionally to solve high-impact problems related to environmental data analysis and reporting.
Responsibilities
- Design and implement machine learning models for real-world applications
- Collaborate with data scientists and software engineers to integrate models into production
- Evaluate model performance and iterate on improvements
- Work with large, messy datasets to extract meaningful patterns
- Develop data pipelines to support training and inference workflows
- Ensure models are explainable, reliable, and maintainable
- Contribute to architectural decisions for scalable ML systems
- Monitor deployed models for performance and data drift
- Optimize inference latency and resource usage
- Support data labeling and annotation efforts
- Translate business requirements into technical ML solutions
- Document model design, training processes, and limitations
- Stay current with advancements in machine learning research
- Mentor junior engineers and share best practices
- Work closely with product teams to define success metrics
- Troubleshoot issues in training and deployment pipelines
- Ensure compliance with data privacy and security standards
- Participate in code and model reviews
- Help define and track key performance indicators for ML systems
- Collaborate on tooling to streamline ML experimentation
Compensation
Competitive salary with equity and benefits
Work Arrangement
Hybrid work model with office and remote flexibility
Team
Collaborative team focused on building scalable AI systems
About the Team
We are a multidisciplinary group of engineers, data scientists, and climate experts building tools to help companies measure and reduce their carbon emissions.
Impact
Our models directly influence how organizations understand their environmental footprint, enabling more accurate reporting and better decision-making.
Technology Stack
We use Python, TensorFlow, PyTorch, Apache Airflow, Kubernetes, and Google Cloud Platform to build and deploy our machine learning systems.
Growth and Development
Engineers are encouraged to lead projects, present at conferences, and contribute to open-source initiatives.
Diversity and Inclusion
We are committed to building a diverse team and fostering an inclusive environment for all employees.
Visa sponsorship available for qualified candidates
