- You will join the AI Lab, a team whose mission is to validate high-value emerging AI and automation technologies and de-risk their adoption across dLocal
- This is a rare opportunity to work at the frontier of applied AI in fintech: running rigorous experiments on the latest models and tools, and turning results into decisions that shape how a integral payments company adopts AI
- This is a senior individual-contributor role
- It does not require direct people management, but it carries significant technical influence within the Lab and across the teams that consume its work
- As a Staff AI Engineer in the AI Lab, you own technology scouting, prototyping, and evaluation for dLocal
- You will run instrumented spikes and benchmarking on emerging AI technologies, produce clear recommendations for stakeholders across engineering, business, legal and IT based on your prototypes, and coordinate hand-offs to the teams that take validated technologies into production
- For promising technologies, you will help define the patterns, guardrails, and technical requirements needed for adoption and coordinate the hand-off to the engineering teams responsible for productionizing them
- Technology Scouting & Evaluation
- Run short, instrumented spikes and benchmarking on new models, tools and frameworks: LLMs, agentic systems, vector databases, orchestration frameworks, copilots, assistants and more
- Compare vendor and open-source options, documenting trade-offs across quality, cost, latency, security and integration complexity
- Deliver concise decision memos with clear recommendations: adopt, watch, or avoid
- Evaluation Harnesses & Sandboxes
- Design and maintain evaluation environments (e.g. datasets, prompts, scenarios, telemetry)



to test models under realistic constraints
- Build automation and tooling to measure quality, robustness, latency and cost, including regression tracking over time
- Ensure every evaluated technology has benchmark coverage and a documented risk and limitations view
- Prototyping & Technical Validation
- Build enough of a system to understand how a technology behaves under realistic conditions, not just in vendor demos or isolated examples
- Explore architecture, integration patterns, operational constraints, security boundaries, and failure modes through working prototypes
- Determine what must be true for a proof of concept to become a viable production capability
- Prefer focused prototypes that answer specific technical questions over prematurely building production systems
- Recommendations, Readiness & Hand-offs
- Translate technical findings into clear decision memos for both technical and non-technical stakeholders
- For validated technologies, produce readiness guidance covering recommended patterns, guardrails, known limitations, operational considerations, and integration requirements
- Coordinate hand-offs to the engineering teams responsible for productionization
- Support those teams during the transition when deep context from the evaluation is required, without becoming the permanent owner of the resulting system




- Track what happens after Lab recommendations and use those outcomes to improve future evaluation methods
- Governance, Risk & Standards
- Work with Security, Legal, Compliance and other AI teams to document risk assessments, mitigations and governance recommendations for each evaluated technology
- Maintain checklists, decision templates and lightweight standards reusable across evaluations and by partner teams
- Incorporate learnings from third-party AI tooling already in use, such as external copilots and the AWS AI suite, into adoption guidelines
- Collaboration, Mentoring & Community
- Partner with other AI teams and domain teams to ensure clear boundaries and smooth collaboration
- Participate in hiring as a technical evaluator and culture champion
- Mentor engineers in the Lab and adjacent teams on evaluation methods, benchmarking and experimental design
- Share knowledge through internal write-ups, tech talks and occasional external meetups and conferences

Experience building agentic or multi-step AI systems involving tool use, orchestration, state, retrieval, or external integrations8+ years of software engineering experience, including significant experience operating at senior or Staff-level scopeExperience with observability, telemetry, testing, and benchmarking of complex systemsAbility to reason about system architecture, reliability, scalability, asynchronous workflows, and distributed components where relevantDeep hands-on experience building and evaluating systems based on LLMs and modern AI toolingStrong knowledge of cloud infrastructure, preferably AWS, and the ability to run experimental workl

📌 Staff AI Engineer (Madrid)
🏢 dLocal
📍 Madrid