Staff AI Engineer, dLocal
AI Labs
Emerging markets payments platform
Be an early applicant
Emerging markets payments platform
1001+ employees
Be an early applicant
1001+ employees
Company mission
To enable global merchants to connect seamlessly with billions of emerging market users.
Role
Who you are
- 8+ years of software engineering experience, including significant experience operating at senior or Staff-level scope
- Deep hands-on experience building and evaluating systems based on LLMs and modern AI tooling
- Strong software engineering fundamentals and the ability to rapidly build high-quality experimental systems
- Experience building agentic or multi-step AI systems involving tool use, orchestration, state, retrieval, or external integrations
- Strong knowledge of cloud infrastructure, preferably AWS, and the ability to run experimental workloads securely and cost-consciously
- 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
- Track record of designing experiments or benchmarks that influenced meaningful technical decisions
- Track record designing and running benchmarks that compare AI models and tools under real constraints
- Experience constructing evaluation datasets: task selection, labelling, holdout discipline, and keeping a set useful as models improve
- 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
- Able to reason about statistical significance on small samples, and to state confidence honestly rather than over-reading a result
- Familiarity with regression tracking, telemetry and versioning, so that a result stays reproducible months later
- Able to turn ambiguous "we should try this new thing" ideas into well-scoped evaluation plans with clear hypotheses and metrics
- Comfortable making trade-off calls across quality, latency, cost and vendor lock-in, and documenting them clearly
- Experience writing short, opinionated decision memos that help others move fast
- Can explain technical results to non-specialists in concrete, concise terms
- Experience working with platform, product and operations teams to align evaluations with real use cases
- Able to influence without authority, aligning teams around shared standards and guardrails
- 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
- Builder attitude: you prefer reusable tools, templates and playbooks over one-off work
What the job involves
- 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 global 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
Application process
- Our Talent Acquisition team is invested in creating the best candidate experience possible, so don’t worry, you will definitely hear from us. We will review your CV and keep you posted by email at every step of the process!
Our take
It is clearly evident that there is a massive payments gap between developed countries and emerging economies, with consumers in many countries having little or no access to modern payment options. dLocal was established in Uruguay to address this gap, which it aims to close not only for its neighbouring countries but across the globe.
Through its unique "One dLocal" concept (one direct API, one platform, and one contract), global companies can accept payments, send pay-outs and settle funds globally without separately managing pay-in and pay-out processors. The company also supplies a defense suite and local issuing for virtual and physical pre-paid cards in Latin America, as well as tools for building payments infrastructure for platforms and marketplaces.
Since founding, the company has expanded across Uruguay, the USA, Israel, Brazil and Europe. The company has seen significant growth while remaining confident and committed to its original mission. A notable achievement is dLocal becoming a certified Payment Initiation Service Provider in Brazil, which means it can eliminate unnecessary steps for users in the region.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
25% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Apr 2021
$150m
LATE VC
Sep 2020
$200m
SERIES A
Total funding: $357m
Company HQ
Tres Cruces, Montevideo, Montevideo Department
Leadership
Sergio Fogel
(Co-Founder & CSO)Co-founded multiple companies, such as AstroPay, and is an Official Member of the Forbes Finance Council.
Pedro Arnt
(CEO)Member of the Board of Directors at Allegro who advises at several firms. Previously EVP and CFO at MercadoLibre.com.
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