kernlith
Fleet Space Technologies
Senior Machine Learning Engineer
Remote · Full-time · Australia
- Compensation
- Not defined
- Category
- AI engineering
- Employment
- Full-time
- Workplace
- Remote
Technologies
Python
AWS
About the role
- This role can be based anywhere in Australia with remote first work or in Adelaide or Perth with Hybrid working where our offices are located.
- Critical minerals are the bottleneck in the clean energy transition, and finding them faster is as much a machine learning problem as a geophysics one. At Fleet Space, our satellite-enabled exploration technology helps generate the data and the models to find deposits faster and more efficiently.
- To help continue to build and scale our products we are looking for a Senior Machine Learning Engineer to help build out our ML operations to develop, mature and deploy the models and systems behind that work. You will partner with our another Senior ML Engineer & our Data science team to take their research models from notebooks and prototypes through to production-grade products, and you will own the pipelines, GPU environments and infrastructure those systems run on.
- This is a role with a focus on scaling our ML ops alongside our Senior ML Engineer. You will design leakage-safe evaluation, verify that uncertainty calibration holds as new data lands, and keep every model's lineage reproducible from config through to prediction.
- You will also own our ML experiment platform: MLflow tracking and the model registry, run identity and provenance conventions, and the environments and dependencies that training jobs run in. Where the platform team has shared infrastructure modules you will reuse them, and you will contribute new patterns back.
- What You’ll Do
- Work with the data science team to transition research models into production-ready products, with code quality, scalability and best practice built in rather than retrofitted.
- Build and maintain robust, efficient ML and data pipelines and the infrastructure behind them, including configuring GPU environments for scalable ML systems.
- Own model quality across retrain cycles: leakage-safe evaluation, calibrated uncertainty that holds as new data lands, and reproducible lineage from config to prediction.
- Own the ML experiment platform end to end: MLflow tracking and model registry, run identity and provenance conventions, and the environments and dependencies training jobs run in.
- Partner with the platform team, reusing shared infrastructure modules where they exist and contributing new patterns back.
- Write clean, testable Python (SOLID principles) backed by unit tests and automated end-to-end tests.
- Keep pace with where ML and MLOps are heading, and bring the useful parts back into how we work.
- What We Are Looking For
- Deep expertise in machine learning across modeling, data engineering and architecture, with proven experience as a software engineer focused on ML
- Alignment to our stack; Python (Pandas, NumPy, scikit-learn, TensorFlow, PyTorch), MLflow, AWS, Databricks, Terraform, GitLab, and Zarr and Icechunk for big-array data
- ML Engineers that enjoy and want to focus on ML ops
- Experience with Geospatial data would be a big plus
- Experience in startup environments, building ML products and environments from early stages
- Experience across the full ML lifecycle, from data preprocessing and feature engineering through to training, evaluation and deployment.
- A track record of taking research artifacts (notebooks, scratch apps, prototype models) to production-grade solutions that scale, for example models served as a service.
- Data engineering from first principles: deterministic, idempotent pipelines, schema and contract discipline at data boundaries, and deliberate failure and retry semantics.
- Software delivery from first principles: continuous release with automated gates, observability, and clean design in application architecture.
- Solid grounding in MLOps: model development, deployment and data versioning, ideally in a startup or scale-up environment.
- Strong Python, with the common data science and ML libraries, and solid software engineering fundamentals across data structures, algorithms and design patterns.