ML Engineer
About Diamond Energy, An Energy Bay Brand
At Energy Bay our mission is to design, integrate and operate distributed energy infrastructure that delivers scalable, sustainable outcomes, driving the future of energy transition for partner enterprises and customers.
Established in 2016, Energy Bay offers specialised distributed renewable energy solutions to facilitate enterprise customers and property landlords through the energy transition. Founded on the values of innovation, value creation and social responsibility, Energy Bay is reshaping how energy is delivered across retail, commercial, industrial and mixed‑use precincts.
An exciting opportunity has arisen for an experienced Machine Learning (ML) Engineer. Reporting to the VPP and Control Systems Manager,you’ll own the price forecasting models and optimisation engine behind our battery energy storage (BESS) trading. You’ll build, train and maintain models that forecast NEM energy and FCAS prices, then turn those forecasts into bidding and dispatch decisions that drive revenue across our battery assets. You’ll also keep those models reliable in production and support compliant automated bidding under the NER and AER rebidding rules.
Diamond Energy prides itself on providing sustainable energy solutions to our customers. As a growing company with big dreams, we offer an exceptional working environment where you can expand your experience and be part of a meaningful mission to create a greener and cleaner future.
To learn more about Diamond Energy, please visit https://diamondenergy.com.au/
Energy Bay Culture
Energy Bay has a distinctly unique, transparent, and constructive company culture. We are a team focused on maximising the scale and impact we create in the energy transition. We are seeking to build on our existing culture and create a diverse team with wide range personal and professional experience, with a long-term focus on execution and the highest standards of delivery.
Our Vision: To be Australia’s most trusted partner in integrating distributed energy resources infrastructure and enabling sustainable progress towards the clean energy future.
Our Values: Zero Harm | Enduring Solutions |Customer Driven |Trusted Partner
As The ML Engineer, You Will
At Energy Bay our mission is to design, integrate and operate distributed energy infrastructure that delivers scalable, sustainable outcomes, driving the future of energy transition for partner enterprises and customers.
Established in 2016, Energy Bay offers specialised distributed renewable energy solutions to facilitate enterprise customers and property landlords through the energy transition. Founded on the values of innovation, value creation and social responsibility, Energy Bay is reshaping how energy is delivered across retail, commercial, industrial and mixed‑use precincts.
An exciting opportunity has arisen for an experienced Machine Learning (ML) Engineer. Reporting to the VPP and Control Systems Manager,you’ll own the price forecasting models and optimisation engine behind our battery energy storage (BESS) trading. You’ll build, train and maintain models that forecast NEM energy and FCAS prices, then turn those forecasts into bidding and dispatch decisions that drive revenue across our battery assets. You’ll also keep those models reliable in production and support compliant automated bidding under the NER and AER rebidding rules.
Diamond Energy prides itself on providing sustainable energy solutions to our customers. As a growing company with big dreams, we offer an exceptional working environment where you can expand your experience and be part of a meaningful mission to create a greener and cleaner future.
To learn more about Diamond Energy, please visit https://diamondenergy.com.au/
Energy Bay Culture
Energy Bay has a distinctly unique, transparent, and constructive company culture. We are a team focused on maximising the scale and impact we create in the energy transition. We are seeking to build on our existing culture and create a diverse team with wide range personal and professional experience, with a long-term focus on execution and the highest standards of delivery.
Our Vision: To be Australia’s most trusted partner in integrating distributed energy resources infrastructure and enabling sustainable progress towards the clean energy future.
Our Values: Zero Harm | Enduring Solutions |Customer Driven |Trusted Partner
As The ML Engineer, You Will
- Build, train and maintain price forecasting models for NEM energy (RRP) and FCAS markets across all forecast horizons
- Develop and improve the BESS optimisation engine that converts price forecasts into bidding and dispatch decisions
- Deliver measurable accuracy improvements over baseline and benchmark models each release cycle
- Track forecast performance using agreed metrics, such as MAE/RMSE for point forecasts and pinball loss for probabilistic forecasts
- Develop probabilistic forecasting approaches to capture price spikes and tail events across energy and FCAS markets
- Improve BESS trading revenue and dispatch performance, including revenue capture against a perfect-foresight benchmark
- Design, build and operate data pipelines, ETL processes and cloud-based ML workflows
- Maintain production models using MLOps practices, including experiment tracking, model versioning, automated retraining and performance monitoring
- Ensure automated bidding and rebidding outputs comply with the NER and AER rebidding guidelines
- Test and document models and pipelines to keep post-release defects to a minimum
- Report model performance, assumptions and limitations transparently to trading and commercial stakeholders
- Work closely with trading, engineering, operations and external partners
- Bachelor’s or Master’s degree in a quantitative discipline — Computer Science, Engineering, Mathematics, Statistics, Physics, Econometrics or similar
- 3–5 years’ experience in machine learning, data science or quantitative analytics
- Hands-on experience building, deploying and monitoring models in production, backed by strong Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow), SQL and data pipeline skills
- Demonstrated time-series forecasting experience, ideally electricity price, demand or renewable generation, including probabilistic and tail-event modelling
- Knowledge of the NEM, including RRP, pre-dispatch and P5MIN, the 10 FCAS markets, and NER and AER bidding and rebidding rules (highly desirable)
- Exposure to optimisation modelling, such as MILP and battery dispatch optimisation, and practical MLOps knowledge (desirable)
- Commercially minded, detail-oriented and disciplined in model validation, testing and documentation, with calm, sound judgement under operational pressure
- Excellent communicator who can explain complex analytical results to traders, engineers and non-technical stakeholders, and who works well independently and in a team
- We believe in responsibility and reward you for taking initiative
- We’re a successful, fast-growing company with an entrepreneurial vibe
- Comprehensive training and mentorship from experienced professionals
- New office location, with stunning panoramic views, close to Burnley train station
- We have a great work culture!