Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Australia.

📋 Description

• Design, enhance, and implement machine learning models and systems that facilitate satellite-enabled mineral exploration.

• Collaborate with the Senior ML Engineer and Data Science team to transition research models from notebooks and prototypes into production-ready solutions.

• Create and sustain robust, efficient machine learning and data pipelines, along with the necessary infrastructure.

• Set up GPU environments to support scalable machine learning systems.

• Take ownership of model quality during retraining cycles through safe evaluation methods and calibrated uncertainty assessments.

• Ensure reproducible model lineage from configuration to prediction.

• Manage the ML experiment platform, which includes MLflow tracking, model registry, run identity, and provenance conventions.

• Oversee environments and dependencies for training jobs.

• Collaborate with the Platform team to leverage existing infrastructure modules and contribute new patterns.

• Write clean, testable Python code following SOLID principles, and implement unit tests and automated end-to-end tests.

• Stay updated on advancements in machine learning and MLOps and integrate beneficial practices.

⛳️ Requirements

• In-depth knowledge of machine learning encompassing modeling, engineering, and architecture, with a proven track record as a software engineer specializing in ML.

• Proficiency in Python, including libraries such as Pandas, NumPy, scikit-learn, TensorFlow, PyTorch, MLflow, as well as tools like AWS, Databricks, Terraform, GitLab, Zarr, and Icechunk.

• Experience throughout the entire ML lifecycle, from data preprocessing and feature engineering to training, evaluation, and deployment.

• Background in transforming research artifacts into production-grade, scalable solutions.

• Knowledge of data engineering principles, including deterministic, idempotent pipelines, schema and contract discipline, as well as failure and retry semantics.

• Understanding of software delivery processes, including continuous release, automated gates, observability, and application architecture.

• Strong foundation in MLOps, covering model development, deployment, and data versioning.

• Solid Python and software engineering principles related to data structures, algorithms, and design patterns.

• Practical experience with AWS and Databricks, infrastructure as code using Terraform, and well-architected design principles.

• Familiarity with code and data version control mechanisms, such as GitLab, MLflow, and Icechunk.

• Experience in startup settings where ML products and environments are built from the ground up.

• A proactive, ownership-driven approach.

• Experience with geospatial data is a bonus.

• Familiarity with big-array data, remote sensing workflows, big data technologies, open-source ML projects, and AI-assisted development workflows is a plus.

🏝️ Benefits

• Highly flexible work environment with options for onsite, hybrid, and remote work.

• Equity (ESOP) grants.

• 20 days of annual leave.

• 10 additional Wellness days each year.

• Learning budgets available.

• Confidential psychologist appointments through the Employee Assistance Program.

• Opportunities to engage in the STEM program.