The Land Transport Authority (LTA) is seeking a Computational Design Technologist for a 1-year temporary contract to join the Infrastructure Design & Engineering Division. This role focuses on developing AI digitalisation for LTA Station Design Criteria, specifically through computational parametric design and generative AI projects. The position involves a broad scope of work including software development, user interface design, and back-end documentation for troubleshooting. Key responsibilities include: conducting rule-set authoring to convert design criteria into machine-readable code; building a context library of past station designs; assisting in workshops to establish project goals; and running technical sessions with discipline leads to validate interpretations. Candidates should possess knowledge in Architecture, Civil/Structural Engineering, Building Science, Computer Science, or a related discipline. Required technical skills include parametric analytical thinking and proficiency in Python or JavaScript/TypeScript, with the ability to write and test rule evaluators. Experience with rule or decision engines such as JSON Logic, DMN, or OPA/Rego is highly valued. The role requires active participation in team discussions and workshop coordination. Applicants may be subject to a medical declaration and further assessment as part of the shortlisting process.
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A focused preparation guide, built around this job’s responsibilities and requirements.
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01
Understand the role
What matters most
Rule-set Authoring and Digitization
The core objective is to convert complex LTA Station Design Criteria into machine-readable code. This requires translating regulatory and engineering standards into functional logic, necessitating high proficiency in Python or JavaScript/TypeScript to build and test robust rule evaluators.
Development of Generative AI and Parametric Platforms
You must apply parametric analytical thinking to develop a platform for AI generative digitalization. This involves building a context library from historical station designs and ensuring the software architecture supports both the generative outputs and the necessary user interface.
Cross-Disciplinary Technical Validation
The role requires bridging the gap between technical code and engineering practice. You must facilitate workshops and lead technical sessions with discipline leads to ensure that your coded interpretations of design criteria align with actual civil, structural, and architectural requirements.
Likely challenges to think through
Translating Ambiguous Engineering Criteria into Logic
Engineering standards are often nuanced and subject to interpretation. The challenge lies in creating rigid, machine-readable rule sets that do not oversimplify or misinterpret the original design intent. Prepare by demonstrating experience with decision engines like DMN or OPA/Rego and explaining your process for handling edge cases in rule-based systems.
Managing Stakeholder Expectations in Technical Workshops
You will be coordinating with discipline leads who may not have a background in computational design. The difficulty is effectively communicating how your code validates their work without creating friction. Prepare by highlighting your ability to translate technical constraints into clear, actionable feedback for non-technical stakeholders.
03
Practice your answers
Technical practice
Implementing Rule-Set Authoring
What to coverExplain your approach to converting complex design criteria into machine-readable code. Discuss how you ensure the logic remains accurate to the original intent while being robust enough for a rule evaluator.
Python/JavaScript for Rule Evaluation
What to coverDescribe your process for writing and testing a rule evaluator from scratch. Focus on how you handle edge cases and ensure the script is maintainable and scalable for future design criteria updates.
Utilizing Decision Engines
What to coverDiscuss your experience with rule or decision engines like JSON Logic, DMN, or OPA/Rego. Explain how you would choose the appropriate engine for a specific set of infrastructure design constraints.
Building a Context Library
What to coverHow would you structure a corpus of past station designs to make them useful for generative AI training or reference? Address data normalization, tagging, and how you ensure the quality of the outcomes stored.
Computational Parametric Design
What to coverDescribe how you apply parametric thinking to infrastructure design. Provide an example of how you would define parameters to automate or optimize a specific design element, such as station layout or structural components.
Generative AI in Infrastructure
What to coverHow do you approach integrating generative AI into a regulated engineering environment? Discuss the trade-offs between automated generation and the need for human-in-the-loop validation.
Back-end Documentation for Troubleshooting
What to coverWhat is your strategy for documenting complex rule-based systems? Explain how you create documentation that allows other developers or domain experts to diagnose and fix logic errors effectively.
UI Design for Technical Tools
What to coverWhen building a platform for generative design, how do you design the user interface to be accessible to non-coding discipline leads? Focus on balancing technical complexity with usability for engineers.
Behavioural practice
Validating Technical Interpretations
What to coverDescribe a time you had to explain a complex technical interpretation to a non-technical stakeholder or discipline lead. How did you ensure they understood the implications and validated your approach?
Collaborating in Workshops
What to coverGive an example of how you have assisted in facilitating a workshop to establish project goals. How did you manage conflicting requirements from different engineering disciplines?
Adapting to Regulatory Constraints
What to coverTell me about a time you worked in a highly regulated domain where rules were rigid. How did you ensure your technical solution remained compliant while still achieving the desired innovation?
Handling Ambiguous Design Criteria
What to coverDescribe a situation where the design criteria provided were ambiguous or incomplete. What steps did you take to clarify the requirements before proceeding with your implementation?
02
Sharpen your knowledge
Topics to prepare
Rules-as-Code and Decision Engines
Understand the conceptual framework of converting regulatory design criteria into machine-readable logic. Review the principles of DMN (Decision Model and Notation) or OPA/Rego to prepare for translating LTA station design standards into automated rule sets.
Computational Parametric Design
Study the integration of parametric modeling with generative AI to automate infrastructure design. Focus on how algorithmic constraints can be applied to architectural or civil engineering workflows to ensure design compliance.
Skills to revise
Rule Evaluator Development in Python or JavaScript
Practice writing and testing custom rule evaluators from scratch rather than just editing existing scripts. Create a small project that takes a set of input parameters and validates them against a defined logic set to demonstrate your ability to build robust validation engines.
Technical Workshop Facilitation
Prepare to communicate complex technical interpretations to non-technical discipline leads. Practice summarizing how machine-readable code maps to real-world engineering constraints to ensure your technical solutions align with project goals during validation sessions.
04
Your preparation checklist
Develop a rule-based evaluation prototype
Create a small-scale script in Python or JavaScript that converts a set of simple design constraints into machine-readable logic. Demonstrate your ability to write and test a rule evaluator rather than just executing existing scripts.
Prepare a portfolio of parametric design projects
Select 2-3 past projects that showcase parametric analytical thinking. Be ready to explain how you structured data, handled complex design criteria, and translated architectural or engineering requirements into computational models.
Research rule and decision engine frameworks
Familiarize yourself with the syntax and application of JSON Logic, DMN (Decision Model and Notation), or OPA/Rego. Prepare to discuss how these tools can be applied to digitize regulatory or technical design standards.
Draft a strategy for stakeholder technical validation
Outline a process for conducting technical sessions with non-technical discipline leads. Explain how you would translate complex engineering criteria into clear, validated rules and how you would document these interpretations for troubleshooting.
Prepare questions for the interview panel
Formulate questions regarding the current state of the station design context library, the specific challenges in digitizing LTA design criteria, and how the team balances generative AI outputs with established engineering safety standards.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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