Research Engineer
Salary not listed • Remote • Colombia, Huila, Colombia • Full-time
Posted 7mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Colombia, Huila, Colombia
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Research Engineer
About the job
Turing is seeking a Research Engineer to contribute to the development of cutting-edge AI datasets, reinforcement learning environments, and evaluations for leading AI labs and enterprise clients. This is a hands-on, research-focused technical leadership role where you will collaborate with customer researchers and engineers to define data and environment specifications based on their model and post-training objectives. You will be responsible for driving the production of high-quality data that meets stringent standards for correctness, realism, diversity, difficulty, and measurable model improvement. The role is ideal for individuals with approximately 4-5 years of experience in building and refining deep learning systems, particularly those where data quality, curation, denoising, synthetic data generation, and rigorous evaluation are critical for success. You will operate within one or more specialized capability areas, including coding and software engineering agents, RL environments, multimodal data and reasoning, STEM reasoning, and modern embodied AI agents.
Responsibilities
- Translate research goals into clear data requirements, defining target skills, failure modes, difficulty calibration, coverage, and success metrics.
- Define data quality standards through detailed rubrics, counterexamples, and boundary cases.
- Conduct in-depth audits of produced data to identify subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts.
- Drive iterative data improvements using evidence from error taxonomies, quality metrics, and model behavior analysis.
- Design and build datasets and RL environments, including task suites, ground-truth signals (verifiers, unit tests, reward functions), and environment interfaces (APIs, tool schemas).
- Implement automated validation and filtering systems for deduplication, decontamination, consistency checks, and difficulty/diversity controls.
- Develop synthetic data generation and augmentation pipelines, including programmatic task generators and simulator-driven rollouts.
- Create documentation and data cards detailing dataset intent, limitations, and recommended use.
- Design and execute evaluations to measure the impact of data and environments on model performance.
- Produce analysis connecting data attributes to model outcomes, including pre/post comparisons and error breakdowns.
- Collaborate with cross-functional teams by providing clear specifications, examples, and feedback loops.
- Partner with research teams to run in-house fine-tuning or RL experiments to demonstrate data/environment impact.
Requirements
- Approximately 4-5 years of experience building and improving deep learning systems.
- Experience with data quality, data curation, denoising, synthetic data generation, and rigorous evaluation.
- Experience in one or more of the following areas: coding/SWE agents, RL environments, multimodal data/reasoning, STEM reasoning, or embodied AI/VLM-driven agents.
- Ability to translate ambiguous research goals into concrete data and environment specifications.
- Detail-oriented approach to data auditing and error identification.
- Experience designing and building datasets and RL environments.
- Proficiency in implementing automated validation, filtering, and synthetic data generation systems.
- Experience designing and running evaluations to prove impact.
- Ability to produce analysis connecting data to model outcomes.
- Experience collaborating effectively with cross-functional teams.
- Strong communication skills to provide clear specifications and feedback.