{"id":86149,"url":"https://alion.io/job/turing-research-engineer-2","title":"Research Engineer","company":{"id":13199,"name":"Turing","domain":"turing.com","url":"https://alion.io/company/turing","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":82,"open_postings":7,"ghost_share":0,"stale_share":0.714,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Colombia"],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Java","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OCR","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rust","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tool Use","optional":false},{"name":"VLM","optional":false},{"name":"Agile","optional":true},{"name":"Edge AI","optional":true},{"name":"GDPR","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Reward Modeling","optional":true},{"name":"RLAIF","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-03-23T14:50:12Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-17T23:39:11Z","board_verified":true,"closed_at":null,"days_open":208,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":207},"description":"About Turing\nTuring’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. \n*This is a remote role and can be performed anywhere in Colombia.*\nThe Role\nWe 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.\nThis 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.\nThis 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:\n Coding and software engineering agents (repositories, unit tests, debugging, tool use, code reviews, long-horizon workflows)\n RL environments and verifier-based training (tasks, rewards/verifiers, trajectories, evaluation harnesses)\n Multimodal data and reasoning (text + images + documents + tables/charts; optional audio/video)\n STEM reasoning (math, physics, chemistry, bio, engineering - solution verification and error analysis)\n Modern embodied AI / VLM-driven agents (vision-language(-action) models, embodied task suites, tool/sensor/action abstractions, long-horizon interaction data)\nWhat You’ll Do\n1) Own data and environment quality from an AI researcher perspective\nTranslate ambiguous research goals into clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics.\nDefine what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases (what to include vs. exclude).\nPerform deep, detail-oriented audits of produced data: spot subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts.\nDrive iterative improvements using evidence: error taxonomies, slice-based quality metrics, and model-behavior-informed refinements.\n2) Design and build datasets and RL environments for your capability area(s)\nContribute to or lead the design of:\n Task suites (single-step and long-horizon workflows)\n Ground-truth signals (verifiers, unit tests, structured checks, reward functions, automatic validators)\n Environment interfaces (APIs, tool schemas, state abstractions, database schemas, simulator-like dynamics)\nDepending on your mapped capability area(s), you may focus on:\n Coding / SWE agents: data reflecting real development work (codebase navigation, bug localization, patching, tests, code reviews, CI-like constraints, refactors, security fixes).\n Multimodality: tasks that test true multimodal reasoning (chart reading, document QA, UI understanding, diagram-based STEM reasoning, OCR-aware tasks).\n STEM: tasks with verifiable solutions (symbolic checks, reference solvers, numerical validation, step consistency, unit sanity).\n 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).\n3) Build robust validation, denoising, and synthetic data systems\nImplement automated validation and filtering to achieve frontier-grade signal-to-noise:\nDeduplication, decontamination, leakage checks\nConsistency checks (format, schema, invariants)\nDifficulty and diversity controls (coverage, novelty, long-tail)\nDevelop synthetic data generation and augmentation pipelines where appropriate:\nProgrammatic task generators\nControlled perturbations to create hard negatives\nScenario templating with diversity constraints\nSimulator-/tool-driven rollouts for trajectory data\nCreate documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage.\n4) Use evaluations and training runs to prove impact\nDesign and run evals that reflect the customer’s intended usage.\nProduce analysis that connects data to outcomes:\nPre/post comparisons on targeted capability slices\nError breakdowns and “why the model failed” narratives\nAblations to identify which data attributes drive lift\nWhen 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.\n5) Collaborate effectively with large production teams without being ops-heavy\nWork with cross-functional teams (engineers, researchers, QAs, domain SMEs, and large-scale data production groups) by providing:\nClear specs, examples, and edge cases\nFast feedback loops based on audits and quantitative signals\nStructured review processes focused on quality, not throughput alone\nYou 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.\nWho We’re Looking For\n4-5 years of experience building or improving deep learning systems where data quality mattered materially (training, post-training, evals, or agentic systems).\nStrong intuition for the “data ingredients” that drive model improvements: what to collect, what to filter, what to synthesize, and how to measure.\nAbility to communicate clearly with researchers and engineers: turning research objectives into concrete specs, and turning messy outputs into actionable insights.\nDemonstrated ability to be extremely detail-oriented in diagnosing subtle data quality issues and failure modes.\nSolid programming ability with a bias for shipping:\nPython proficiency required\nComfort with SQL/structured data workflows strongly preferred\nFor coding-focused work: proficiency in one or more major languages (e.g., C++, Java, Go, Rust, JS/TS) is a plus\nComfort designing quality systems:\nRubrics, validation scripts, gold sets, sampling strategies\nStatistical checks and slice-based evaluation\nHuman-in-the-loop review loops grounded in measurable criteria\nStrong pluses\nRL or post-training experience (any of: RLHF/RLAIF, verifier training, reward modeling, RL fine-tuning, environment design).\nExperience with agentic evaluation (tool use, multi-step workflows, long-horizon tasks, trajectory analysis).\nMultimodal expertise (document understanding, charts, diagrams, OCR, UI/vision grounding; audio/video optional).\nSTEM depth (math/physics/engineering) with an eye for verifiability and rigorous correctness.\nModern embodied AI / VLM-driven agent experience (vision-language(-action) models, interaction datasets, embodied evals, long-horizon grounding, tool/sensor/action interfaces).\nSystems thinking: ability to “simulate” an application’s API/data schema and design tasks that realistically reflect real-world constraints and workflows.\nWhy Turing\nWork directly with the world’s leading AI labs and enterprises at the cutting edge of post-training and RL environment design.\nReal impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence.\nReal Impact (GDP): the systems you help build and evaluate target high-value workflows across industries, where even incremental improvements translate to significant productivity gains.\nTalent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.\nValues\nWe are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.\nWe work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection\nWe are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.\nAdvantages of joining Turing\nWork 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.\nContribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.\nBring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.\nCollaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.\nMove at the pace of AI innovation, with the speed, ownership, and impact of a startup.\nTuring 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.\nFor applicants from the European Union, please review Turing's GDPR notice here.","description_format":"text","description_chars":10321,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Colombia","iso":null,"kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Recruiting & Staffing","Freelance & Talent Marketplaces","AI Training Data & Annotation"],"lifecycle":[{"event":"open","at":"2026-03-23T14:50:12Z"}],"visa":[],"liveness":{"score":5,"band":"cold","label":"Long shot","p_open":1,"p_active":0.166,"p_room":0.28,"age_days":207,"expected_fill_days":27,"reasons":["conf:1","velocity","win:tail","crowd:"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/turing-research-engineer-2","json_url":"https://alion.io/job/turing-research-engineer-2.json","meta":{"generated_at":"2026-10-18T00:39:51Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":1162,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":13199},"rest":"https://alion.io/mcp/rest/get_company?id=13199"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fturing-research-engineer-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fturing-research-engineer-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fturing-research-engineer-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/turing-research-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fturing-research-engineer-2"}]}