We’re building a statistically rigorous platform for extracting richer insights and deeper signals from complex human text data. Our architecture combines statistical frameworks with machine learning and large language models, overcoming human biases and ensuring reproducible results and consistent quality at scale.
Earlier this year, we closed a $1.3m pre-seed round led by a specialist AI fund. We’re already working with major pharmaceutical and finance groups. Now we’re accelerating, scaling enterprise deployments and building the engineering foundation for what comes next. As one of our early hires, you’ll help shape WholeSum’s technology, culture, and future.
For too long, organisations have settled for glorified word clouds, crude sentiment tracking or unreliable AI summaries. We're changing that, making complex human data an asset and not a burden. This is a hard, meaningful problem – come help us solve it.
We’re hiring an engineer to build the infrastructure and APIs that power WholeSum’s analysis engine. You’ll design the systems that ingest, structure, and serve insights from massive volumes of data. You’ll define the architecture, data models, and scaling patterns that make our platform fast, reliable, and auditable.
You’ll work directly with our founding engineer and our CTO, and collaborate with enterprise clients to ensure our systems are robust, scalable, and genuinely useful.
You have several years of professional software engineering experience and are comfortable owning projects end-to-end.
You're a generalist at heart - happy writing application code one day and configuring infrastructure the next.
You have strong experience with our core stack: Python + FastAPI, React and AWS.
Deep AWS experience - networking, IAM, ECS/EKS, Lambda, RDS, or similar managed services.
You have hands-on experience with DevOps practices - CI/CD, Infrastructure as Code (Terraform), containerisation, and cloud deployment patterns.
You take initiative, flag problems early, and propose solutions rather than waiting to be told what to do.
You care about building products that are secure, observable, and actually used.
Enterprise integration experience and collaboration with platform-specific contractors.
Experience with monitoring, logging, and alerting in production environments (e.g. Datadog, CloudWatch, Grafana).
Familiarity with working alongside ML/data pipelines and the infrastructure challenges they bring.
Experience building in early-stage or startup environments where scope is broad and ambiguity is high.
Interest in AI reproducibility and auditability.
