Role Overview

We are building next-generation revenue optimization and algorithmic dispatch capabilities for Battery Energy Storage Systems (BESS) and Virtual Power Plant (VPP) platforms across residential, commercial & industrial (C&I), and utility-scale energy applications in overseas electricity markets.

This role focuses on developing and deploying forecasting and optimization algorithms into live production environments, with direct impact on energy trading performance, dispatch quality, fleet optimization, and revenue generation across distributed energy assets.

The position sits at the intersection of:

Machine Learning & Reinforcement Learning
Power Market Optimization
Energy Trading
Forecasting & Dispatch
Distributed Energy Systems

This is a highly applied role with strong production ownership. The algorithm team owns the full lifecycle from research and offline evaluation to live deployment, monitoring, tuning, and continuous optimization.

Candidates with strong applied optimization or ML system backgrounds from adjacent industries are also welcome, even without prior energy market experience.

Key Responsibilities

Develop forecasting and optimization algorithms for electricity markets, battery dispatch, and VPP operations
Design and improve strategies for energy trading, ancillary services participation, and distributed energy optimization
Build and deploy production-grade ML, RL, MPC, or optimization models into live fleet environments
Improve algorithm performance, operational stability, and revenue generation across distributed energy assets
Collaborate closely with EMS, cloud platform, firmware, and operations teams to support large-scale deployment and continuous optimization
Support algorithm adaptation for overseas electricity markets, including Australia NEM and European power markets

Preferred Technical Background

Strong Python development skills
Experience with machine learning, reinforcement learning, MPC, operations research, or optimization systems
Familiarity with optimization solvers such as Gurobi or CPLEX
Experience with time-series forecasting frameworks
Experience deploying algorithms into production systems
Familiarity with cloud-native or distributed system environments is a plus

Requirements

5+ years of hands-on experience in algorithm engineering, optimization, applied machine learning, or related fields
Strong experience in building production-grade optimization or ML systems
Experience with forecasting, optimization, dispatch, recommendation, control, or decision-making systems
Strong engineering mindset with end-to-end ownership mentality
Experience in energy storage, VPP, EMS, electricity markets, or energy trading is preferred
Experience in deregulated electricity markets (e.g. Australia NEM, Europe, ERCOT, CAISO) is a strong plus
Candidates from adjacent domains such as quantitative optimization, autonomous systems, logistics optimization, trading systems, or large-scale real-time decision systems are encouraged to apply

AI Optimization Engineer