Research Engineer

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