Research Engineer

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.

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).

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.

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.

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.

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

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.

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.

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.

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.