About the Role

L3 Business Group is growing its R&D software team and is looking for an AI / Optimisation Engineer to own the intelligence and optimisation layer of our AI-powered energy management system.

You will start by building real-world impact from day one — developing and refining physics-informed optimisation algorithms that control HVAC systems intelligently, achieving measurable energy savings without requiring large historical datasets. As our data platform matures and real-world deployment data accumulates, your role evolves naturally toward machine learning — building adaptive models that improve continuously over time, and eventually owning the full intelligence output layer that powers our commercial energy analytics products.

This is a role for an engineer who is equally comfortable reasoning about control systems and thermodynamics as they are writing clean, production-grade Python. You will work directly with our System Architect and Data & ML Platform Engineer in a small, technically serious team.

Key Responsibilities

• Design, implement, and refine physics-informed and rule-based optimisation algorithms for HVAC energy control.
• Build a decision fusion layer that combines outputs from multiple control algorithms, resolving conflicts and producing unified, actionable control outputs
• Define and implement performance metrics for optimisation quality — energy savings, temperature compliance, system efficiency — and build monitoring dashboards that track these in production
• Work closely with the Data & ML Platform Engineer to define data requirements for ML model development — specifying what data needs to be collected, tagged, and stored to enable future model training
• Progressively build machine learning models as real-world deployment data accumulates — transitioning from rule-based optimisation toward data-driven adaptive intelligence
• Design and implement the consumption intelligence output layer — energy breakdown by appliance class, site efficiency scoring, and optimisation recommendation engine
• Build the retrospective analysis capability — enabling historical simulation of what the optimisation system would have achieved at a site over a given period
• Develop scenario modelling logic — allowing projection of energy and cost outcomes under different operating conditions
• Ensure all algorithm logic and model decisions are clearly documented — both for IP defensibility and for knowledge transfer to the team

Requirements

• Minimum 2–3 years of experience in algorithm development, optimisation, applied AI/ML, or a closely related engineering role; OR a strong fresh graduate with demonstrable projects in optimisation, control systems, or applied machine learning
• Strong proficiency in Python — primary language for all algorithm and model development
• Solid understanding of optimisation principles — rule-based control, constraint satisfaction, multi-objective optimisation
• Familiarity with control systems concepts — setpoint control, feedback loops, load balancing — or willingness to learn quickly with domain guidance
• Experience building and evaluating machine learning models — regression, classification, time-series forecasting
• Understanding of energy systems, thermodynamics, or HVAC operation is a strong advantage — but genuine curiosity and ability to learn the domain is equally valued
• Strong analytical and problem-solving ability — you must be able to reason about why an algorithm is or isn't working and iterate systematically
• Good documentation discipline — algorithm logic and decision rationale must be recorded clearly
• Able to work independently and collaboratively with a small technical team
• Singapore Citizen or Permanent Resident preferred

Good to have:

• Experience with building energy management, HVAC control, or smart building systems
• Familiarity with reinforcement learning or model predictive control (MPC)
• Experience with time-series data analysis and anomaly detection
• Familiarity with weather data APIs and climate-adjusted modelling
• Exposure to IoT sensor data pipelines or edge-to-cloud data architectures

Career Growth

You will work directly with our System Architect from day one, with full ownership of the algorithm and intelligence layer of our system. As the platform scales and data accumulates, your role evolves from rule-based optimisation engineering toward full ML-driven intelligence ownership — you will be the person who decides how our system learns, adapts, and improves over time.

The growth path is toward Lead AI / Optimisation Engineer — taking architectural ownership of the full intelligence platform, mentoring engineers joining the team, and becoming the primary technical authority on how our system generates value for every deployed site. In this company, the intelligence you build is the product.