Job Description & How to Apply Below
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Job Description
We are building a team of engineers in Edmonton, AB, Canada to develop a first-of-its-kind synthetic persona platform — an AI system that models human behavior, preferences, and decision-making s role sits at the intersection of software engineering, data engineering and applied AI: you will design the pipelines that power the personas and , the modelsand the platform deployments that bring them to life.
This is not a maintenance role. The platform is being built from the ground up, and every technical decision you make will shape what it becomes. You will work alongside researchers, data scientists, and product engineers in a small, high-trust team where curiosity and ownership are the norm.
What You Will Do
Design and build a scalable platform and the data pipelines that ingest, transform, and serve structured and unstructured data to AI models
Develop and iterate on AI/ML components — including LLM-based agents, embedding models, and behavioral simulation layers — that power synthetic persona generation
Architect and maintain the data infrastructure underpinning persona modeling: feature stores, vector databases, data lakes, and real-time serving layers
Collaborate with researchers to translate persona logic and behavioral frameworks into working system components
Write production-quality code, participate in code reviews, and contribute to engineering standards for the team
Monitor model and pipeline performance in production; identify and resolve issues proactively
Contribute to system design discussions and help shape the technical roadmap
Qualifications
Must be able to commute to our Edmonton, AB, Canada office
Core Technical Skills
3–5 years of hands‑on experience in software engineering, data engineering, ML engineering, or a closely related role
Proficiency in Python and at least one data processing framework (Spark, dbt, Airflow, Prefect, or similar)
Experience building and deploying ML models or AI components in a production environment
Familiarity with LLMs and modern AI tooling: prompt engineering, fine‑tuning, RAG pipelines, and agent frameworks (Lang Chain, Lang Graph, Llama Index, CrewAI, or equivalent)
Solid understanding of data modeling, schema design, and the tradeoffs between different storage paradigms (relational, document, vector, columnar)
Experience with cloud data infrastructure — AWS, GCP, or Azure — and comfort operating in a cloud‑native environment
Systems & Engineering Mindset
Ability to reason about system architecture: latency, throughput, scalability, and data consistency tradeoffs
Experience with APIs, microservices, or event‑driven architectures (Kafka, Pub/Sub, or similar)
Comfort working across the stack — from raw data ingestion through to model serving and API exposure
Strong debugging instincts and a habit of writing observable, testable code
How You Work
Intrinsically motivated — you pursue hard problems because they interest you, not because someone handed you a ticket
Comfortable with ambiguity; you can move forward when the requirements are still forming
Collaborative by default — you ask questions, share context early, and bring others along
You read papers, experiment on weekends, and have opinions about how AI systems should be built
Nice to Have
Experience with Snowflake
Experience with behavioral modeling, simulation, or agent-based systems
Background in NLP, computational social science, or user modeling
Contributions to open-source AI or data tooling
Familiarity with synthetic data generation techniques or privacy-preserving ML
Experience working in a startup or early‑stage product environment
Additional Information
Synthetic personas are one of the most technically interesting and practically consequential challenges in applied AI right now. The platform you help build will be used to simulate human decision-making in ways that have real product and business impact. You will have direct influence over architectural decisions, meaningful ownership of your components, and a front‑row seat to a research area that is evolving fast.
Must be able to work on‑site at our Edmonton, AB, Canada location
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