Your opportunity

This role sits within a climate technology organization operating at the intersection of atmospheric science, machine learning, and large-scale data systems. The team builds predictive and operational ML platforms designed to reduce real-world environmental risk by preventing high-impact wildfire events before they occur.

You will contribute to machine learning systems that support scientific research, operational decision-making, and production deployment. The work spans early-stage model development through to scalable, automated ML workflows that can operate reliably in high-stakes, real-world conditions.

Initially, the focus is hands-on model training and experimentation. Over time, the role expands toward owning and evolving the end-to-end ML lifecycle, including automation, deployment, and monitoring as the platform matures.

Key responsibilities 

• Model Development: Develop, train, and evaluate machine learning models, including data preprocessing, feature engineering, validation strategies, and documentation of results

• Experiment Management: Manage datasets, experiments, and artifacts to ensure traceability, reproducibility, and scientific rigour

• Pipeline Automation: Design and implement automated pipelines for data ingestion, training, evaluation, and model versioning

• ML Infrastructure: Build and maintain CI/CD workflows for machine learning systems, ensuring scalability, reliability, and repeatability

• Deployment Support: Support model deployment and monitoring across batch and service-based workloads, collaborating with engineering and science partners

Tech Stack

• Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn

• Languages: Python

• MLOps Tooling: MLflow, Weights and Biases, DVC

• Orchestration: Airflow, Prefect, Dagster

• Cloud Platforms: AWS, GCP, or Azure

• Containers and Compute: Docker, Kubernetes or managed equivalents

• CI/CD: GitHub Actions, GitLab CI

Your know-how

• Hands-on experience developing and training machine learning models in production-oriented environments

• Strong understanding of data preprocessing, feature engineering, and model evaluation techniques

• Experience building and maintaining ML pipelines and automated workflows

• Proficiency in Python for data and machine learning applications

• Experience working with at least one major cloud platform, including compute, storage, and basic networking concepts

• Working knowledge of containerization and deploying ML workloads via APIs or batch systems

It’s a bonus if

• You have experience monitoring model performance, data quality, or drift in production systems

• You are familiar with ML observability or data validation frameworks

• Your background includes using managed ML services such as SageMaker, Vertex AI, or Azure ML

• You have worked with infrastructure-as-code tools such as Terraform or CloudFormation

• You have supported machine learning systems operating in scientifically driven or high-impact real-world domains

Interested in learning more?

Please send your resume or LinkedIn profile URL to talent@lutrapartners.com with “MLOPs Engineer” as the subject line. One of our talent partners will be in contact shortly.