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.
As a Staff Engineer in AI R&D, you set the technical direction for agentic and LLM-based systems, raise the engineering bar of the teams around you, and take on the hardest design and production problems. You will join one of our AI R&D teams, matched to your profile and to the company's most pressing needs, and you will work across team boundaries whenever the problem requires it.
You will also take technical leadership of important initiatives: shaping the approach, guiding the team through execution from design to production, and keeping the work aligned with peers across engineering and with the business areas it serves. You will engage with those stakeholders directly, so that decisions are made quickly and with full context.
The areas where you could contribute include:
AI-native software delivery: agents that support the full software lifecycle, from planning and coding to review, testing and incident response.
Business process automation: a self-service platform where business areas configure and own agentic automations, with human review and full traceability.
AI platform foundations: shared services such as LLM access and routing, secure tool and system integrations, reusable agent components and governed skills and plugins.
Where you land is decided during the selection process, based on your experience, your interests and the needs of each team.
What Will I Be Doing?
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.
Work directly with peers and business stakeholders to understand needs, agree on scope and priorities, and keep expectations aligned as the work evolves.
Translate business goals into technical plans, milestones and trade-offs, and raise risks early.
Make sure initiatives reach production with outcomes that can be measured and explained to the business.
Architecture & Technical DirectionLead the design and architecture of AI systems, including agentic workflows, retrieval, tool use, orchestration and state management, built by one or several teams.
Define reference architectures, patterns and engineering standards that other teams adopt and reuse.
Make and document trade-off decisions across quality, latency, cost, security and vendor dependency.
Solve the most complex technical problems in your area and unblock the teams around you.
Quality & Evaluation of AI SystemsEstablish how AI behavior is measured: evaluation datasets, regression tracking, quality metrics and acceptance criteria for agents and LLM-based features.
Ensure systems are tested against realistic cases, not only demos, before and after they reach production.
Define the telemetry needed to understand cost, latency, quality and outcomes end to end.
Production ExcellenceLead operational practices: reliability, scalability, resilience, observability and incident learning for AI services.
Examine production behavior, identify structural improvements and drive them to completion with the owning teams.
Keep cost under control through routing, caching, context management and capacity decisions.
Security, Governance & Compliance by DesignDesign controlled access for agents that act on internal systems: permissions, guardrails, audit trails and human-in-the-loop checkpoints.
Work with Security, Legal, Compliance and IT so that governance requirements are built into reusable components and not rebuilt for every use case.
Multiplying ImpactMentor engineers and tech leads, and raise the technical level of the teams you work with.
Turn what you learn into playbooks, templates, shared libraries and documentation that others can use without you.
Communicate clearly with technical and non-technical stakeholders, and align teams around shared standards.
Share knowledge through internal write-ups and tech talks, and occasionally through external meetups and conferences.
What Skills Do I Need?
8+ years of software engineering experience, including significant experience operating at senior or Staff-level scope.
2+ years of hands-on experience building and operating systems based on LLMs, ideally in production.
Experience designing agentic or multi-step AI systems involving tool use, orchestration, state, retrieval or external integrations.
Strong foundations in distributed systems, including synchronous and asynchronous communication, and in software architecture principles and practices.
Solid knowledge of cloud infrastructure, preferably AWS, and of running services securely and cost-consciously.
Experience with observability, testing and evaluation of complex systems.
Technical leadershipTrack record of technically leading initiatives that involve multiple teams, from design to production.
Comfortable working directly with business stakeholders and engineering peers to agree on scope, priorities and outcomes.
Ability to understand trade-offs, make decisions with incomplete information and stay effective in crisis situations.
Experience defining engineering standards and best practices that teams actually adopt.
Able to influence without authority, and to explain technical decisions to non-specialists in concrete, concise terms.
Nice to haveExperience with agent frameworks and protocols such as LangChain, LangGraph, the Claude Agent SDK or MCP.
Experience with Kubernetes and containerized execution environments.
Experience with LLM gateways, model routing and cost management.
Background in machine learning or applied research.
Experience in regulated environments such as payments or financial services.
MindsetBuilder attitude: you prefer reusable platforms and tools over one-off solutions.
Curious and biased toward experimentation, combined with disciplined measurement and risk awareness.
Comfortable with ambiguity in a fast-moving field, and able to structure your own work and keep stakeholders informed.
Besides the tailored benefits we have for each country, dLocal will help you thrive and go that extra mile by offering you:
- Flexibility in how you work: We focus on impact and productivity over fixed hours. This means our teams have flexible schedules and, depending on your role and location, you will combine self‑managed focus time with moments of in‑person connection in our collaboration hubs.
- Fintech industry: work in a dynamic and ever-evolving environment, with plenty to build and boost your creativity.
- Referral bonus program: our internal talents are the best recruiters - refer someone ideal for a role and get rewarded.
- Work From Anywhere: Team members can work while traveling for up to 3 months every year.
Skills Required
- 8+ years of software engineering experience, including significant senior or Staff-level experience
- 2+ years of hands-on experience building and operating LLM-based systems, ideally in production
- Experience designing agentic or multi-step AI systems involving tool use, orchestration, state, retrieval, or external integrations
- Strong foundations in distributed systems, including synchronous and asynchronous communication
- Strong knowledge of software architecture principles and practices
- Knowledge of cloud infrastructure, preferably AWS, and securely and cost-consciously running services
- Experience with observability, testing, and evaluation of complex systems
- Track record of technically leading initiatives involving multiple teams from design through production
- Experience collaborating directly with business stakeholders and engineering peers on scope, priorities, and outcomes
- Ability to make decisions with incomplete information and remain effective during crisis situations
- Experience defining engineering standards and best practices adopted by teams
- Ability to influence without authority and explain technical decisions to non-specialists
- Experience with agent frameworks or protocols such as LangChain, LangGraph, Claude Agent SDK, or MCP
- Experience with Kubernetes and containerized execution environments
- Experience with LLM gateways, model routing, and cost management
- Background in machine learning or applied research
- Experience in regulated environments such as payments or financial services
What We Do
dLocal started with one goal – to close the payments innovation gap between global enterprise companies, and customers in emerging economies. We have over 900 payment methods, in more than 40 countries. With the ability to accept local payment methods and facilitate cross-border fund settlement worldwide, our merchants reach billions of underserved consumers in the high-growth markets of Africa, Asia, and Latin America. dLocal offers the ideal payment solutions for global commerce: Payins: Accept local payment methods Payouts: Compliantly send funds cross-border Defense Suite: Manage fraud effectively dLocal for Platforms: Unify your platform’s payment solution Local Issuing: Localize payments for your gig-economy workers, suppliers, and partners
