Junior Applied AI Engineer

About SynthEngine

SynthEngine is building a data-backed AI platform that uses synthetic populations to explore how people may think, make decisions, interact, and respond to changing real-world scenarios. The platform combines government and survey data, structured representations of people and their environments, quantitative decision models, and AI-powered reasoning and interaction systems. We are looking for a hands-on engineer to help turn our existing prototype into a more reliable, integrated, and testable product.

What you will work on

• Build and improve the core platform: Work with an existing Python-based codebase, understand its architecture, trace how data moves through the system, and resolve bugs and integration issues.
• Connect AI and simulation components: Help integrate the synthetic population, quantitative decision engine, scenario processing, and AI reasoning components so that they work together consistently.
• Work with structured data: Build and improve data-processing pipelines for demographic, economic, survey, and other structured datasets. Maintain data quality, source attribution, and clear documentation.
• Develop and integrate AI capabilities: Work with LLM APIs and AI-agent workflows where appropriate. Help implement structured inputs and outputs, context retrieval, scenario reasoning, and controlled interactions between simulation components.
• Test and validate behaviour: Write automated tests, reproduce bugs, compare simulation runs, check that changes do not break existing functionality, and help build repeatable evaluation workflows.
• Make the system easier to operate: Improve APIs, error handling, logging, documentation, and development workflows so that the product becomes easier to maintain and extend.
• Use AI coding tools responsibly: Use AI-assisted development tools to work efficiently, while understanding, testing, and verifying the code you produce.

Requirements & Tech Stack

• Good working knowledge of Python.
• Ability to read, understand, debug, and modify an existing codebase.
• Familiarity with APIs, JSON, data structures, and databases.
• Basic understanding of software testing, Git, and version control.
• Ability to break complex problems into smaller, testable tasks.
• Willingness to learn unfamiliar technical concepts and document your work.
• A practical project portfolio demonstrating that you can build working software.

Good to have

• Experience with FastAPI, Flask, Django, or similar backend frameworks.
• Experience integrating LLM APIs or building AI-powered applications.
• Exposure to multi-agent systems, knowledge graphs, data pipelines, or simulation.
• Familiarity with PostgreSQL, Docker, automated testing, or cloud deployment.
• Experience using AI coding assistants to develop and debug real applications.

Note: You do not need to have experience in every preferred area. Strong Python fundamentals, sound engineering habits, and the ability to learn are more important.

Qualifications

• We welcome early-career engineers and recent graduates with strong practical projects.
• We value curiosity, disciplined debugging, honest technical judgment, and the ability to explain complicated problems clearly.
• Please share your resume, GitHub profile or project portfolio, and a short explanation of a technical project you personally built.

This is a hands-on engineering role. It is not a prompt-writing position, a data-entry role, or a requirement to train a large language model from scratch.