Staff AI Engineer (AI Labs)

Join dLocal's AI Lab as a Staff AI Engineer, where you'll validate emerging AI technologies and shape the company's AI adoption strategy. This senior individual-contributor role involves technology scouting, prototyping, evaluation, and coordination with engineering teams. You'll work at the forefront of applied AI in fintech, running experiments on the latest models and tools.

Responsibilities

• Participer à la validation des technologies émergentes en matière d'IA et d'automatisation, en menant des expériences rigoureuses et en prenant des décisions éclairées.
• Diriger le scouting technologique, le prototypage et l'évaluation des technologies d'IA pour l'entreprise, en produisant des recommandations claires pour les parties prenantes.
• Collaborer avec d'autres équipes d'IA et de domaine pour assurer des frontières claires et une collaboration fluide, et partager vos connaissances à travers des écrits internes et des conférences.
• Working knowledge of LLM-as-judge methods and their failure modes, alongside human evaluation, inter-annotator agreement, and a view on when each is appropriate
• Experience writing short, opinionated decision memos that help others move fast
• Comfortable making trade-off calls across quality, latency, cost and vendor lock-in, and documenting them clearly
• Builder attitude: you prefer reusable tools, templates and playbooks over one-off work

Requirements

• Experience building agentic or multi-step AI systems involving tool use, orchestration, state, retrieval, or external integrations
• 8+ years of software engineering experience, including significant experience operating at senior or Staff-level scope
• Experience with observability, telemetry, testing, and benchmarking of complex systems
• Ability to reason about system architecture, reliability, scalability, asynchronous workflows, and distributed components where relevant
• Deep hands-on experience building and evaluating systems based on LLMs and modern AI tooling
• Strong knowledge of cloud infrastructure, preferably AWS, and the ability to run experimental workloads securely and cost-consciously
• Track record of designing experiments or benchmarks that influenced meaningful technical decisions
• Strong software engineering fundamentals and the ability to rapidly build high-quality experimental systems
• Experience constructing evaluation datasets: task selection, labelling, holdout discipline, and keeping a set useful as models improve
• Familiarity with regression tracking, telemetry and versioning, so that a result stays reproducible months later
• Able to reason about statistical significance on small samples, and to state confidence honestly rather than over-reading a result
• Track record designing and running benchmarks that compare AI models and tools under real constraints
• Able to turn ambiguous "we should try this new thing" ideas into well-scoped evaluation plans with clear hypotheses and metrics
• Can explain technical results to non-specialists in concrete, concise terms
• Able to influence without authority, aligning teams around shared standards and guardrails
• Experience working with platform, product and operations teams to align evaluations with real use cases
• Curious and biased toward experimentation, combined with disciplined measurement and risk awareness
• Comfortable in a small, high-leverage team without embedded PMs. You structure your own work and keep stakeholders informed