Research Engineer

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows.

• Own data and environment quality from an AI researcher perspective: Translate ambiguous research goals into clear data requirements; define what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases; perform deep, detail-oriented audits of produced data; drive iterative improvements using evidence.
• Design and build datasets and RL environments for your capability area(s): Contribute to or lead the design of task suites, ground-truth signals, and environment interfaces. Depending on your mapped capability area(s), you may focus on Coding / SWE agents, Multimodality, STEM, or Modern embodied AI / VLM-driven agents.
• Build robust validation, denoising, and synthetic data systems: Implement automated validation and filtering to achieve frontier-grade signal-to-noise; develop synthetic data generation and augmentation pipelines; create documentation and data cards.
• 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; when needed, run in-house fine-tuning or RL-style experiments.
• Collaborate effectively with large production teams without being ops-heavy: Work with cross-functional teams by providing clear specs, examples, and edge cases; fast feedback loops; structured review processes.

• 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.
• Operate in one or more of the following capability areas: Coding and software engineering agents, RL environments and verifier-based training, Multimodal data and reasoning, STEM reasoning, Modern embodied AI / VLM-driven agents.

• This is a remote role and can be performed anywhere in Brazil.
• Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
• Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
• Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.
• Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
• Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.