Research infrastructure
Research Engineer
This role lives at the intersection of research and infrastructure. You will build and maintain the systems that make AI visibility research possible at scale — prompt runners, answer capture pipelines, normalization layers, and the internal tooling that keeps quality high as models and assistants change.
Résumé du rôle
Build the systems that run strategic prompts at scale, capture raw model output reliably, and feed clean data into evaluation pipelines.
Pourquoi ce rôle existe
The research pipeline is the foundation everything else sits on. Without reliable prompt execution and output capture, the product has nothing to evaluate. We need someone who treats the pipeline as a product, not just plumbing.
90 premiers jours
Map and document the current prompt execution and capture pipeline end to end.
Pourquoi ce rôle existe
The research pipeline is the foundation everything else sits on. Without reliable prompt execution and output capture, the product has nothing to evaluate. We need someone who treats the pipeline as a product, not just plumbing.
Ce sur quoi vous travaillerez
- Design and operate prompt execution systems across multiple AI assistants at scale.
- Build output capture, normalization, and storage layers that handle variability in model behavior.
- Own the reliability and observability of the research data pipeline end to end.
- Work closely with the research team to translate evaluation requirements into efficient system behavior.
À quoi ressemble un bon fit
- Strong systems and backend engineering instincts applied to data pipelines and async workloads.
- Experience building reliable, observable systems that handle high cardinality and partial failures gracefully.
- Comfort reasoning about LLM output formats, rate limits, latency, and prompt execution logistics.
- A product mindset: you treat the pipeline as something users depend on, not just infrastructure.
Ce qui vous enthousiasmera ici
- Building the infrastructure that makes serious AI research operationally possible.
- Owning reliability for a data layer that directly determines product quality.
- Working on a system that touches every corner of the product.
90 premiers jours
- 01Map and document the current prompt execution and capture pipeline end to end.
- 02Identify and fix the top two reliability or observability gaps in the research data path.
- 03Ship at least one improvement that materially reduces the cost or latency of a core research workflow.
Processus de recrutement
Le processus est volontairement court, direct et ancré dans le travail réel.
- 1
Candidature
Envoyez-nous votre parcours, votre travail pertinent et pourquoi ce rôle vous correspond.
- 2
Conversation de base
Un échange centré sur votre travail, votre jugement et le rôle.
- 3
Deep dive du rôle
Une discussion ou un exercice qui ressemble davantage au travail réel qu'à une boucle d'entretien générique.
- 4
Conversation avec le fondateur
Un dernier échange sur le niveau d'exigence, l'ambition et ce que serait la réussite ici.
- 5
Décision
Nous bouclons clairement et avançons vite quand la conviction est là.
Besoin de contexte avant de postuler ? [email protected]
Research Engineer
Le rôle est visible sur le site. Les candidatures s'ouvrent dès que le poste Dover correspondant est actif.
Les candidatures restent fermées jusqu'à l'activation du poste Dover correspondant. D'ici là, vous pouvez écrire à [email protected].