Research Engineer

Colombia, Huila, Colombia

About Turing

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. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com.

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

About the Position

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.

Responsibilities

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

3. 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 where appropriate:
   • 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:
   • 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:
   • 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.

Requirements

• 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:
• Comfort designing quality systems:

Nice to Have

• 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.

We Offer

• 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.

About the Company

Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

For applicants from the European Union, please review Turing's GDPR notice here.