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 dLocalThis 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 general payments company adopts AIThis is a senior individual-contributor roleIt does not require direct people management, but it carries significant technical influence within the Lab and across the teams that consume its workAs a Staff AI Engineer in the AI Lab, you own technology scouting, prototyping, and evaluation for dLocalYou 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 productionFor 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 themTechnology Scouting & EvaluationRun short, instrumented spikes and benchmarking on new models, tools and frameworks: LLMs, agentic systems, vector databases, orchestration frameworks, copilots, assistants and moreCompare vendor and open-source options, documenting trade-offs across quality, cost, latency, security and integration complexityDeliver concise decision memos with clear recommendations: adopt, watch, or avoidEvaluation Harnesses & SandboxesDesign and maintain evaluation environments (e.g. datasets, prompts, scenarios, telemetry) to test models under realistic constraintsBuild automation and tooling to measure quality, robustness, latency and cost,



including regression tracking over timeEnsure every evaluated technology has benchmark coverage and a documented risk and limitations viewPrototyping & Technical ValidationBuild enough of a system to understand how a technology behaves under realistic conditions, not just in vendor demos or isolated examplesExplore architecture, integration patterns, operational constraints, security boundaries, and failure modes through working prototypesDetermine what must be true for a proof of concept to become a viable production capabilityPrefer focused prototypes that answer specific technical questions over prematurely building production systemsRecommendations, Readiness & Hand-offsTranslate technical findings into clear decision memos for both technical and non-technical stakeholdersFor validated technologies, produce readiness guidance covering recommended patterns, guardrails, known limitations, operational considerations, and integration requirementsCoordinate hand-offs to the engineering teams responsible for productionizationSupport those teams during the transition when deep context from the evaluation is required, without becoming the permanent owner of the resulting systemTrack what happens after Lab recommendations and use those outcomes to improve future evaluation methodsGovernance, Risk & StandardsWork with Security, Legal, Compliance and other AI teams to document risk assessments,



mitigations and governance recommendations for each evaluated technologyMaintain checklists, decision templates and lightweight standards reusable across evaluations and by partner teamsIncorporate learnings from third-party AI tooling already in use, such as external copilots and the AWS AI suite, into adoption guidelinesCollaboration, Mentoring & CommunityPartner with other AI teams and domain teams to ensure clear boundaries and smooth collaborationParticipate in hiring as a technical evaluator and culture championMentor engineers in the Lab and adjacent teams on evaluation methods, benchmarking and experimental designShare knowledge through internal write-ups, tech talks and occasional external meetups and conferencesExperience 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 workloads securely and cost-consciouslyTrack record of designing experiments or benchmarks that influenced meaningful technical decisionsStrong software engineering fundamentals and the ability to rapidly build high-quality experimental systemsExperience constructing evaluation datasets: task selection, labelling, holdout discipline, and keeping a set useful as mode

📌 Staff AI Engineer (Madrid)
🏢 dLocal
📍 Madrid