---
title: 'Research Engineer at Turing'
canonical: 'https://feeny.ai/job/research-engineer-turing-colombia-huila-pra2rtbwfybc'
type: 'job'
last_seen: '2026-09-09'
---

# Research Engineer at Turing

- **Company:** Turing
- **Location:** Colombia Huila, Colombia
- **Work type:** remote
- **Posted:** 2026-03-23
- **Last confirmed live:** 2026-09-09
- **Apply:** https://job-boards.greenhouse.io/turing/jobs/5819908004

## Job description

## 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](http://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)
- STEM reasoning (math, physics, chemistry, bio, engineering – solution verification and error analysis)
- 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.

1. 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 (long-horizon tasks, instruction following grounded in visual context, robust action selection, safety/constraint adherence, adversarial state coverage).

1. 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.

1. 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.

1. 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 not primarily 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
- Comfort designing quality systems:
- Rubrics, validation scripts, gold sets, sampling strategies
- Statistical checks and slice-based evaluation
- Human-in-the-loop review loops grounded in measurable criteria

Strong pluses

- 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.

## 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.

For applicants from the European Union, please review [Turing's GDPR notice here](https://www.turing.com/policy).

## About Turing

## Company Overview
- **One-liner**: Turing is an AI company that partners with leading AI labs and enterprises to advance frontier model capabilities and deploy end-to-end AI systems for mission-critical workflows.
- **Entity Type**: Private (raised $111M at a $2.2B valuation)
- **Headquarters**: San Francisco, California, United States (also offices in Palo Alto, CA)
- **Founded**: 2018
- **Founders**: Jonathan and Vijay (computer science alumni)

## Core Business
- Primary industry: Artificial Intelligence, Technology, Information and Internet
- Target customers: B2B, including Fortune 500 companies, frontier AI labs, and enterprises
- Mission: “Accelerate superintelligence to drive real economic progress”
- Operates across multiple areas: reasoning, coding, multimodality, multilinguality, STEM, agentic behavior, and frontier knowledge

## Products & Services
- **Frontier AI Lab Partnerships**: Collaborate with leading AI labs to build data, evaluations, and benchmarks that push the AI frontier forward (e.g., RL environments, data generation for pre-training/post-training).
- **Enterprise AI Systems**: Build and deploy end-to-end AI systems for mission-critical enterprise workflows.
- **Turing Talent Cloud**: AI-vetted pool of 4M+ software engineers, data scientists, and STEM experts used to train models and build AI applications.
- **ALAN Platform**: AI-powered platform for matching, managing talent, and generating high-quality human and synthetic data for model evals, fine-tuning, RLHF, and benchmarking.

## Market Standing
- **Valuation/Market Cap**: $2.2 billion (most recent funding round amount not explicitly dated, but cited on careers page)
- **Key Metric**: Total funding of $111M (from the $2.2B valuation round)
- **Notable Investors/Partners**: Adam D’Angelo (Facebook’s first CTO), Foundation Capital (backers of Netflix, Uber), executives from Microsoft, and others. Partners include Fortune 500 companies.
- **Growth Signals**: 48.1% YoY employee growth (reaching 3,896 employees), #1 on The Information’s “Top 50 Most Promising B2B Companies”, named one of Fast Company’s top 10 companies in the Workplace category, active job postings of 2,700 (+66.9% YoY).

## Competitive Advantages
- AI-vetted talent pool of over 4 million professionals, enabling rapid scaling of AI projects.
- Proprietary ALAN platform that orchestrates talent matching, data generation, and model evaluation.
- Deep partnerships with frontier AI labs, including work on large-scale RL environments and data generation.
- History of being built by engineering leaders from Facebook, Microsoft, and other top tech companies.
- Strong brand recognition and trust among enterprise clients (zero-risk trial for hiring).

## Strategic Focus
- Advancing artificial general intelligence (AGI) by improving model capabilities in reasoning, coding, agentic behavior, and multimodality.
- Expanding enterprise AI deployment across industries (e.g., financial services, robotics, STEM).
- Scaling the talent cloud and ALAN platform to support pre-training, post-training, and AI application development.
- Growing the workforce with a focus on research, engineering, and data science roles.

## Why Work Here
- **Culture**: Emphasizes “client first”, “startup speed”, “AI forward”, and “work with joy”. Described as a fast-paced, innovative environment.
- **Remote/Hybrid**: Many roles are remote-first globally, with offices in San Francisco and Palo Alto for in-person collaboration.
- **Perks**: Work on frontier AI technology with top labs; flexible, well-paid remote work; opportunities to contribute to superintelligence; strong career growth (48% YoY headcount increase).
- **Engineering Culture**: Heavy focus on R&D, research engineering, and data science; roles include Principal Research Engineer, Robotics Engineer, and Head of Applied AI Lab.
- **Workforce Diversity**: Operates in 100+ countries, with employees from India, US, Pakistan, Nigeria, Brazil, etc.

## Sources
1. [careers.turing.com](https://careers.turing.com/)
2. [job-boards.greenhouse.io/turing](http://job-boards.greenhouse.io/turing)
3. [linkedin.com/company/turingcom](https://www.linkedin.com/company/turingcom)
4. [welcome.turing.com/about/](https://welcome.turing.com/about/)
5. [turing.com/jobs](https://www.turing.com/jobs)

## Other roles at Turing

- [Strategy & Operations Manager](https://feeny.ai/job/strategy-operations-manager-turing-bengaluru-3sjr1tg8nr6r) — Bengaluru, India
- [Senior Technical Recruiter - Research Engineering Pod](https://feeny.ai/job/senior-technical-recruiter-research-engineering-pod-turing-united-states-kw26pb4fdb62) — United States
- [Senior DevOps & Infrastructure Engineer](https://feeny.ai/job/senior-devops-infrastructure-engineer-turing-colombia-huila-jwhzmnmnvt79) — Colombia Huila, Colombia / São Paulo, Brazil
- [Strategic Project Lead - Code](https://feeny.ai/job/strategic-project-lead-code-turing-india-sfk0wnms76fw) — India
- [Senior Product Marketing Manager](https://feeny.ai/job/senior-product-marketing-manager-turing-palo-alto-california-4g8zeesfvfdc) — Palo Alto California, United States / San Francisco California, United States
- [Product Manager, Finance](https://feeny.ai/job/product-manager-finance-turing-new-york-new-york-crn55k6792ry) — New York New York, United States
- [People Systems & Operations Manager](https://feeny.ai/job/people-systems-operations-manager-turing-brazil-colombia-huila-colombia-fehabm9q83pv) — Brazil / Colombia Huila, Colombia
- [Strategic Project Lead, Software Engineering](https://feeny.ai/job/strategic-project-lead-software-engineering-turing-new-york-new-york-12cbjjyd8yr2) — New York New York, United States / San Francisco California, United States / Seattle Washington, United States
- [AI Engagement Lead](https://feeny.ai/job/ai-engagement-lead-turing-new-york-new-york-fe82mpebdcw7) — New York New York, United States
- [Executive Assistant to the Founder & CEO](https://feeny.ai/job/executive-assistant-to-the-founder-ceo-turing-palo-alto-california-tpz3vh0az1qd) — Palo Alto California, United States / San Francisco California, United States