Design and build high-quality datasets and reinforcement learning environments for frontier AI models across coding agents, multimodal reasoning, STEM, and embodied AI systems. Translate research goals into data specifications, audit outputs for subtle errors, develop validation and synthetic data pipelines, and run experiments proving impact. Requires 4–5 years improving deep learning systems where data quality mattered, strong Python skills, comfort with SQL, and detail-oriented quality systems thinking. Remote in Colombia.
Written from this posting by Neural Jobs AI. The full description is below.
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.*
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)
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)
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 (long-horizon tasks, instruction following grounded in visual context, robust action selection, safety/constraint adherence, adversarial state coverage).
3) Build robust validation, denoising, and synthetic data systems
Implement automated validation and filtering to achieve frontier-grade signal-to-noise:
Deduplication, decontamination, leakage checks
Consistency checks (format, schema, invariants)
Difficulty and diversity controls (coverage, novelty, long-tail)
Develop synthetic data generation and augmentation pipelines where appropriate:
Programmatic task generators
Controlled perturbations to create hard negatives
Scenario templating with diversity constraints
Simulator-/tool-driven rollouts for trajectory data
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:
Pre/post comparisons on targeted capability slices
Error breakdowns and “why the model failed” narratives
Ablations to 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.
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:
Clear specs, examples, and edge cases
Fast feedback loops based on audits and quantitative signals
Structured review processes focused on quality, not throughput alone
You are expected to be highly engaged in reviewing and improving outputs from large annotation/creation efforts, but notprimarily responsible for hiring, staffing, or people operations.
Who We’re Looking For
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:
Python proficiency required
Comfort with SQL/structured data workflows strongly preferred
For coding-focused work: proficiency in one or more major languages (e.g., C++, Java, Go, Rust, JS/TS) is a plus
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.
Why Turing
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.
Values
We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
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.
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.
Enterprise Software · 500+ Members · Palo Alto, CA, United States
Turing supplies the human expertise behind frontier model training, pairing AI labs with engineers and domain specialists for data, evaluation and reinforcement learning from human feedback.
It also runs its own applied AI engineering practice, deploying models inside client systems.
