Staff Engineer, AI R&D

Why Join dLocal?dLocal is the financial infrastructure powering global commerce in the world's fastest-growing markets. The biggest companies in the world trust us to unlock growth in 60+ countries across emerging markets—moving money where others see complexity. We don't just process payments; we are architects of payment ecosystems and partners in our customers' expansion. You'll work alongside 1,300+ teammates from 40+ nationalities and tackle global challenges from day one.
What Will I Be Doing?
Technical Leadership of Initiatives
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Act as the technical lead of AI initiatives, from the first definition of the problem through delivery in production, working alongside the engineers and team leads who execute them.
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Work directly with peers and business stakeholders to understand needs, agree on scope and priorities, and keep expectations aligned as the work evolves.
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Translate business goals into technical plans, milestones and trade-offs, and raise risks early.
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Make sure initiatives reach production with outcomes that can be measured and explained to the business.
Architecture & Technical Direction
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Lead the design and architecture of AI systems, including agentic workflows, retrieval, tool use, orchestration and state management, built by one or several teams.
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Define reference architectures, patterns and engineering standards that other teams adopt and reuse.
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Make and document trade-off decisions across quality, latency, cost, security and vendor dependency.
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Solve the most complex technical problems in your area and unblock the teams around you.
Quality & Evaluation of AI Systems
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Establish how AI behavior is measured: evaluation datasets, regression tracking, quality metrics and acceptance criteria for agents and LLM-based features.
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Ensure systems are tested against realistic cases, not only demos, before and after they reach production.
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Define the telemetry needed to understand cost, latency, quality and outcomes end to end.
Production Excellence
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Lead operational practices: reliability, scalability, resilience, observability and incident learning for AI services.
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Examine production behavior, identify structural improvements and drive them to completion with the owning teams.
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Keep cost under control through routing, caching, context management and capacity decisions.
Security, Governance & Compliance by Design
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Design controlled access for agents that act on internal systems: permissions, guardrails, audit trails and human-in-the-loop checkpoints.
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Work with Security, Legal, Compliance and IT so that governance requirements are built into reusable components and not rebuilt for every use case.
Multiplying Impact
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Mentor engineers and tech leads, and raise the technical level of the teams you work with.
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Turn what you learn into playbooks, templates, shared libraries and documentation that others can use without you.
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Communicate clearly with technical and non-technical stakeholders, and align teams around shared standards.
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Share knowledge through internal write-ups and tech talks, and occasionally through external meetups and conferences.
What Skills Do I Need?
Technical depth
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8+ years of software engineering experience, including significant experience operating at senior or Staff-level scope.
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2+ years of hands-on experience building and operating systems based on LLMs, ideally in production.
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Experience designing agentic or multi-step AI systems involving tool use, orchestration, state, retrieval or external integrations.
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Strong foundations in distributed systems, including synchronous and asynchronous communication, and in software architecture principles and practices.
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Solid knowledge of cloud infrastructure, preferably AWS, and of running services securely and cost-consciously.
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Experience with observability, testing and evaluation of complex systems.
Technical leadership
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Track record of technically leading initiatives that involve multiple teams, from design to production.
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Comfortable working directly with business stakeholders and engineering peers to agree on scope, priorities and outcomes.
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Ability to understand trade-offs, make decisions with incomplete information and stay effective in crisis situations.
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Experience defining engineering standards and best practices that teams actually adopt.
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Able to influence without authority, and to explain technical decisions to non-specialists in concrete, concise terms.
Nice to have
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Experience with agent frameworks and protocols such as LangChain, LangGraph, the Claude Agent SDK or MCP.
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Experience with Kubernetes and containerized execution environments.
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Experience with LLM gateways, model routing and cost management.
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Background in machine learning or applied research.
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Experience in regulated environments such as payments or financial services.
Mindset
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Builder attitude: you prefer reusable platforms and tools over one-off solutions.
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Curious and biased toward experimentation, combined with disciplined measurement and risk awareness.
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Comfortable with ambiguity in a fast-moving field, and able to structure your own work and keep stakeholders informed.