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.
Sintesi del ruolo
Build the systems that run strategic prompts at scale, capture raw model output reliably, and feed clean data into evaluation pipelines.
Perché questo ruolo esiste
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.
Primi 90 giorni
Map and document the current prompt execution and capture pipeline end to end.
Perché questo ruolo esiste
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.
Su cosa lavorerai
- 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.
Com'è un forte 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.
Cosa ti entusiasmerà qui
- 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.
Primi 90 giorni
- 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.
Processo di hiring
Il processo è volutamente corto, diretto e ancorato al lavoro reale.
- 1
Candidatura
Mandaci il tuo percorso, il lavoro rilevante e perché questo ruolo ha senso per te.
- 2
Conversazione iniziale
Una conversazione focalizzata sul tuo lavoro, sul tuo giudizio e sul ruolo.
- 3
Approfondimento specifico del ruolo
Una discussione o un esercizio che assomiglia più al lavoro reale che a un loop generico di colloqui.
- 4
Conversazione con il founder
Un confronto finale su standard, ambizione e su come apparirebbe il successo qui.
- 5
Decisione
Chiudiamo il loop con chiarezza e ci muoviamo velocemente quando c'è convinzione.
Hai bisogno di contesto prima di candidarti? [email protected]
Research Engineer
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Le candidature restano chiuse finché il job corrispondente su Dover non viene attivato. Nel frattempo puoi scrivere a [email protected].