About the role
This is a founding applied-AI role. You'll be building our core data pipeline and intelligence layer with the founding team from 0 to 1 You'll be tackling our largest technical challenges across technologies.
Responsibilities:
- Design and build data pipelines that capture and turn high volumes of system activity into structured, queryable data.
- Turn raw activity streams into processes: sessionise event logs, cluster recurring sequences, and use LLMs to label and summarise what's happening.
- Build the evaluation backbone from scratch: stand up synthetic data generation pipelines that produce labelled scenarios to measure accuracy and catch regressions.
- Own data quality and privacy.
- Partner closely with the founders and the rest of engineering to ship features end to end.
Requirements:
- 1-4 years of experience in data engineering, AI/ML engineering, or backend work with a data focus (some of this can be project or research experience).
- Strong in Python and/or TypeScript, comfortable in SQL, and able to build data pipelines you can trust.
- Hands-on experience working with LLMs' structured output, prompting, and wrangling non-determinism while keeping behaviour reliable.
- A practical sense for evaluation: you know that "it looks right" isn't the same as "it's measurably right".
- Care about data privacy and handling sensitive information responsibly.
- Comfort with ambiguity and a real appetite to own a hard, open-ended problem.
Nice to Have:
- Background in process mining, sequence / event-log analysis, or workflow analytics.
- Deeper PostgreSQL: window functions, partitioning, pg_cron, query performance.
- Embeddings and vector search (pgvector) or semantic retrieval.
- Familiarity with cloud infrastructure.
- Any prior early-stage startup experience.
- Degree in computer science or a related field.