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.
Resumen del rol
Build the systems that run strategic prompts at scale, capture raw model output reliably, and feed clean data into evaluation pipelines.
Por qué existe este rol
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.
Primeros 90 días
Map and document the current prompt execution and capture pipeline end to end.
Por qué existe este rol
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.
En qué trabajarás
- 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.
Cómo se ve un gran encaje
- 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.
Qué te entusiasmará aquí
- 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.
Primeros 90 días
- 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.
Proceso de hiring
El proceso es intencionalmente corto, directo y anclado en el trabajo real.
- 1
Solicitud
Envíanos tu trayectoria, trabajo relevante y por qué este rol tiene sentido para ti.
- 2
Conversación base
Una conversación centrada en tu trabajo, tu criterio y el rol.
- 3
Profundización específica del rol
Una conversación o ejercicio que se parezca al trabajo real más que a un bucle genérico.
- 4
Conversación con el fundador
Una charla final sobre estándares, ambición y cómo sería el éxito aquí.
- 5
Decisión
Cerramos el proceso con claridad y nos movemos rápido cuando hay convicción.
¿Necesitas contexto antes de aplicar? [email protected]
Research Engineer
Este rol ya es visible en la web. Las candidaturas se activan en cuanto el puesto correspondiente en Dover esté activo.
Las candidaturas permanecen cerradas hasta que el puesto correspondiente en Dover se active. Mientras tanto puedes escribir a [email protected].