This is a remote role and can be performed anywhere in Brazil.

The Role

We are looking for a Research Engineer to help deliver frontier-quality datasets, RL environments, and evaluations that improve state-of-the-art models for leading AI labs and enterprise clients.

This is a hands-on, research-facing technical leadership role. You will work directly with customer researchers/engineers to translate their model and post-training goals into concrete data and environment specifications, and drive the production of data that meets extremely high standards for correctness, realism, diversity, difficulty, and measurable model lift.

This role is designed for candidates with roughly 4 to 5 years of experience building and improving deep learning systems, especially where strong results depend on data quality, data curation, denoising, synthetic data generation, and rigorous evaluation. You’ll operate in one or more of the following capability areas:

• Coding and software engineering agents (repositories, unit tests, debugging, tool use, code reviews, long-horizon workflows)
• RL environments and verifier-based training (tasks, rewards/verifiers, trajectories, evaluation harnesses)
• Multimodal data and reasoning (text + images + documents + tables/charts; optional audio/video)
• STEM reasoning (math, physics, chemistry, bio, engineering – solution verification and error analysis)
• Modern embodied AI / VLM-driven agents (vision-language(-action) models, embodied task suites, tool/sensor/action abstractions, long-horizon interaction data)

What You’ll Do

1) Own data and environment quality from an AI researcher perspective

• Translate ambiguous research goals into clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics.
• Define what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases (what to include vs. exclude).
• Perform deep, detail-oriented audits of produced data: spot subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts.
• Drive iterative improvements using evidence: error taxonomies, slice-based quality metrics, and model-behavior-informed refinements.

2) Design and build datasets and RL environments for your capability area(s)

• Contribute to or lead the design of:
• Task suites (single-step and long-horizon workflows)
• Ground-truth signals (verifiers, unit tests, structured checks, reward functions, automatic validators)
• Environment interfaces (APIs, tool schemas, state abstractions, database schemas, simulator-like dynamics)
• Depending on your mapped capability area(s), you may focus on:
• Coding / SWE agents: data reflecting real development work (codebase navigation, bug localization, patching, tests, code reviews, CI-like constraints, refactors, security fixes).
• Multimodality: tasks that test true multimodal reasoning (chart reading, document QA, UI understanding, diagram-based STEM reasoning, OCR-aware tasks).
• STEM: tasks with verifiable solutions (symbolic checks, reference solvers, numerical validation, step consistency, unit sanity).
• Modern embodied AI / VLM-driven agents: interaction data and environments for vision-language(-action) models (long-horizon tasks, instruction following grounded in visual context, robust action selection, safety/constraint adherence, adversarial state coverage).

3) Build robust validation, denoising, and synthetic data systems

• Implement automated validation and filtering to achieve frontier-grade signal-to-noise:
• Deduplication, decontamination, leakage checks
• Consistency checks (format, schema, invariants)
• Difficulty and diversity controls (coverage, novelty, long-tail)
• Develop synthetic data generation and augmentation pipelines where appropriate:
• Programmatic task generators
• Controlled perturbations to create hard negatives
• Scenario templating with diversity constraints
• Simulator-/tool-driven rollouts for trajectory data
• Create documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage.

4) Use evaluations and training runs to prove impact

• Design and run evals that reflect the customer’s intended usage.
• Produce analysis that connects data to outcomes:
• Pre/post comparisons on targeted capability slices
• Error breakdowns and “why the model failed” narratives
• Ablations to identify which data attributes drive lift
• When needed, run in-house fine-tuning or RL-style experiments (or partner with research) to demonstrate that the data/environment improves model behavior in measurable ways.

5) Collaborate effectively with large production teams without being ops-heavy

• Work with cross-functional teams (engineers, researchers, QAs, domain SMEs, and large-scale data production groups) by providing:
• Clear specs, examples, and edge cases
• Fast feedback loops based on audits and quantitative signals
• Structured review processes focused on quality, not throughput alone
• You are expected to be highly engaged in reviewing and improving outputs from large annotation/creation efforts, but not primarily responsible for hiring, staffing, or people operations.

Who We’re Looking For

• 4–5 years of experience building or improving deep learning systems where data quality mattered materially (training, post-training, evals, or agentic systems).
• Strong intuition for the “data ingredients” that drive model improvements: what to collect, what to filter, what to synthesize, and how to measure.
• Ability to communicate clearly with researchers and engineers: turning research objectives into concrete specs, and turning messy outputs into actionable insights.
• Demonstrated ability to be extremely detail-oriented in diagnosing subtle data quality issues and failure modes.
• Solid programming ability with a bias for shipping:
• Python proficiency required
• Comfort with SQL/structured data workflows strongly preferred
• For coding-focused work: proficiency in one or more major languages (e.g., C++, Java, Go, Rust, JS/TS) is a plus
• Comfort designing quality systems:
• Rubrics, validation scripts, gold sets, sampling strategies
• Statistical checks and slice-based evaluation
• Human-in-the-loop review loops grounded in measurable criteria

Strong pluses

• RL or post-training experience (any of: RLHF/RLAIF, verifier training, reward modeling, RL fine-tuning, environment design).
• Experience with agentic evaluation (tool use, multi-step workflows, long-horizon tasks, trajectory analysis).
• Multimodal expertise (document understanding, charts, diagrams, OCR, UI/vision grounding; audio/video optional).
• STEM depth (math/physics/engineering) with an eye for verifiability and rigorous correctness.
• Modern embodied AI / VLM-driven agent experience (vision-language(-action) models, interaction datasets, embodied evals, long-horizon grounding, tool/sensor/action interfaces).
• Systems thinking: ability to “simulate” an application’s API/data schema and design tasks that realistically reflect real-world constraints and workflows.

Why Turing

• Work directly with the world’s leading AI labs and enterprises at the cutting edge of post-training and RL environment design.
• Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence.
• Real Impact (GDP): the systems you help build and evaluate target high-value workflows across industries, where even incremental improvements translate to significant productivity gains.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

As part of Fireblocks, Dynamic represents the next evolution of non-custodial wallet infrastructure. Dynamic is Fireblocks’ embedded wallet platform, purpose-built for developers who want to bring crypto and stablecoin experiences into modern applications without complexity. Together, Fireblocks and Dynamic are building the most complete, secure, and developer-friendly wallet infrastructure in the industry by combining institutional-grade security with consumer-grade experiences.

About the Team

Dynamic was founded in 2021 with a simple vision: every app and website will have a wallet component. That vision is rapidly becoming reality. Wallets are no longer limited to crypto-native products, they are becoming foundational infrastructure for payroll, remittances, and global payments. Dynamic powers wallet-based experiences for leading crypto and fintech teams including Kraken, zerohash, Magic Eden, and more.

Dynamic builds and maintains developer-first SDKs that enable teams to seamlessly integrate embedded wallet functionality directly into their applications. These embedded wallets allow end users to create, manage, and use wallets without leaving the product experience, making wallets feel like a native part of modern fintech and crypto apps.

As part of Fireblocks, the Dynamic team continues to build and scale the infrastructure and developer tooling that has onboarded 50M+ wallets across crypto and fintech applications. In this context, Dynamic powers the application-layer wallet experience and developer tooling, complementing Fireblocks’ core custody, security, and transaction infrastructure. The team operates with a strong product mindset and deep focus on security, reliability, scalability, and exceptional developer experience. Engineers at Dynamic build secure-by-default systems and approach their work with a strong customer focus, balancing technical excellence with real-world user needs. You will be joining a passionate group that moves fast, works with the biggest names in payments and fintech, and is defining how the next generation of financial applications are built.

What You’ll Do

• Design and implement high-quality native mobile SDKs that enable developers to integrate embedded wallet functionality into their applications, with a strong focus on API ergonomics, performance, and reliability.
• Own SDK features end-to-end: from API design and architecture, through implementation and testing, to release, versioning, and developer documentation.
• Build and maintain secure, reliable SDK components for wallet operations, including cryptographic operations, blockchain interactions, and security-sensitive flows.
• Define and evolve the SDK architecture to support long-term maintainability, backward compatibility, and adoption across diverse customer applications.
• Build sample applications and demos that showcase SDK capabilities and serve as integration references for customers.
• Create clear, comprehensive SDK documentation and integration guides.
• Work with developer relations and support teams to address customer integration challenges.
• Raise the bar for quality and operational excellence by establishing best practices around testing, observability, releases, and code health.
• Contribute to a culture of ownership and execution, leading by example through hands-on work, code reviews, documentation, and knowledge sharing.

What You’ll Bring

• 5+ years of software engineering experience, with deep focus on mobile development and shipping production-grade SDKs.
• Demonstrated track record of building and shipping high-quality code, with work you can point to and discuss in depth.
• Strong hands-on experience with iOS (Swift) and/or Android (Kotlin); experience with React Native or other cross-platform frameworks, and familiarity with all.
• Experience building mobile SDKs or shared libraries, including API design, versioning, packaging, and long-term maintenance across platforms.
• Experience with blockchain-enabled mobile systems, including wallet flows, signing, and on-chain interactions.
• Experience working on security-sensitive mobile software, such as key management, secure storage, cryptography, or financial products.
• Strong grasp of mobile engineering best practices, including CI/CD, automated testing, release management, and observability.
• A get-things-done mindset with the ability to independently own problems and deliver end-to-end solutions.
• Comfortable making long-term architectural decisions while remaining pragmatic and adaptable.
• B.Sc. in Computer Science / Engineering or equivalent practical experience.
• Remote role in a distributed team; preference for candidates in the Americas time zone.

Big Advantages

• Rust and/or WebAssembly (Wasm) experience, particularly building cross-platform SDKs consumed by mobile apps.
• Experience designing shared core logic used across iOS and Android.
• Experience with advanced mobile security primitives (secure hardware, MPC, low-level crypto).
• Prior experience owning or founding a mobile platform or SDK from scratch.

About us

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

About AB InBev Growth Group

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

What you'll do:

• Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps and align architecture and standards with long-term goals.
• Own technical end-to-end delivery of ML pipelines—from architecture and data/feature pipelines through model development, deployment, and production scaling—in partnership with data, platform, and product teams.
• Design and standardize the ML model development framework (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
• Build and implement observability across the model lifecycle (training and production) via monitoring, logging, and alerting for performance, quality, and drift.
• Ensure optimized deployments that minimize cost and maximize quality (e.g., scaling, resource allocation, inference optimization) while meeting SLAs.

What you'll need:

• Bachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus.
• Relevant experience with ML platform architecture (e.g., feature stores, model registries, training and inference pipelines).
• Systems thinking (scalability, reliability, latency, cost)
• Software design patterns for building maintainable, production-ready ML systems.
• Python, PySpark, and SQL. Java, Rust, and NodeJS are a plus
• Kubernetes, Databricks, Terraform, Azure Devops (Git), Azure Cloud, ML Frameworks (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.) and ML Libraries (Scikit Learn, Pytorch, Tensor Flow, ONNX, etc.)

More about you:

• You´re motivated by building up world-class solutions that impact millions of users all over the world;
• You´re creative when solving problems and is continuously seeking improvements for processes and solutions;
• You can communicate clearly with your team and the company to identify issues and demands;
• You have the autonomy to recognize the current and new priorities, evaluating the impact of their outcomes in the final result.

What We Offer:

• Performance based bonus*
• Attendance Bonus* 
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance

*Rules applied

Equal Opportunity & Affirmative Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

The following fields are optional, but anticipate the information for your registration*.

Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com