Research Engineer, Frontier Data

Turing, Brazil +1

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 a frontier AI lab client. You will work directly with the client's researchers and engineers, turning inbound requests and post-training goals into concrete technical proposals and data/environment specifications, and then owning the technical execution: building the quality and verification systems that ensure what we deliver meets extremely high standards for correctness, realism, diversity, difficulty, and measurable model lift.

What you will do

Own data and environment quality from an AI researcher perspective
Respond to inbound requests from the client and translate ambiguous, evolving research goals into a technical proposal and clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics.
Provide the technical feasibility assessment, assumptions, acceptance criteria, and evaluation requirements needed to scope the contract or statement of work.
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: group failures (for example, by semantic similarity) to find the underlying gap, then turn that into concrete instructions for the production team — better few-shot examples, explicit counterexamples, clearer guidance on what's causing rejections.
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.
Build robust validation, denoising, and synthetic data systems
Build project-specific verification systems that combine deterministic checks, model-based evaluators calibrated against gold sets, and targeted human review.
Implement automated validation and filtering to achieve frontier-grade signal-to-noise: deduplication, decontamination, leakage checks, consistency checks, difficulty and diversity controls.
Develop synthetic data generation and augmentation pipelines where appropriate: programmatic task generators, controlled perturbations, scenario templating, simulator-/tool-driven rollouts.
Create documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage.
Use evaluations and training runs to prove impact
Design and run evals that reflect the client's intended usage.
Produce analysis that connects data to outcomes: pre/post comparisons, error breakdowns, ablations that 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.
Collaborate effectively with large production teams without being ops-heavy
Give technical direction to the distributed expert network producing the data, by providing clear specs, examples, edge cases, and fast feedback loops based on audits and quantitative signals.
You own technical translation, acceptance criteria, evaluator and verifier design, quality diagnosis, and the technical release recommendation. The Strategic Project Lead owns production execution, staffing, throughput, project cost, and recovery.
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 — that sits with the SPL.
Communicate directly with the client on technical findings, quality risks, and recovery options. Align changes to scope, schedule, or external commitments with the account and delivery owners.

Research Engineer, Frontier Data

The role

Research Engineer, Frontier Data

The role

What you will do

Contribute to or lead the design of

Depending on your mapped capability area(s), you may focus on

What you need

Strong pluses

Why Turing

Values

Advantages of joining Turing

About Turing

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