What we do

We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications.

The Role

We are looking for a Frontier Data Lead – RL to own the end-to-end lifecycle of RL environment projects, spanning environment design, task generation, reward/verifier design, quality, and delivery to frontier AI labs and enterprise clients. 

This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and build RL environments specific to their needs. You will lead teams of engineers, subject matter experts (e.g. Finance expert, if you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this.

What You’ll Do 

• End-to-End Ownership: Lead RL Environment projects end-to-end for one or more clients, ensuring the environment you and your team create matches the client’s spec, surpasses quality expectations, and is delivered on time. 
• Data Quality: Ensure the RL environments you produce, the data that goes into those environments, and the the data generated from them (e.g. agent trajectories and reward scores) meet frontier standards for realism, difficulty, diversity. 
• Team building and enablement: Work with your Ops counterparts to build the team of full-stack engineers, back-end engineers, domain experts, QAs, data creators, reviewers, and others you’ll need to deliver the environment on time. You’ll interview, hire, onboard, train, retain talent for your team 
• Process Leadership: Set the process that each of the above team members follows to generate environment code, database schemas, seed data, tasks, and verifiers; set up quality rubrics, automated validation scripts, and human-in-the-loop review processes for every aspect of the environment and data for the environment.
• Customer Interaction: Own customer relationships for your RL Environment project(s), and act as the primary point of contact for leading AI labs, providing regular updates, asking for feedback, and identifying opportunities to grow project scope and revenue. 
• Sales & Solutioning: participate in client solutioning conversations alongside our sales teammates; understand the needs of researchers at AI labs, translate those needs into environment goals 
• Evals & Post-training: Demonstrate proof of value for your environments by running inhouse RL fine tuning experiments to measure model performance lifts on agent trajectories; or by producing eval reports of frontier models on your environment and tasks

Who We’re Looking For

• RL & Post-training experience: familiarity with RL fine tuning, verifier/reward design, and/or environment design 
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.
• Systems thinking + Database/API design: ability to ‘simulate’ the data schema and API interface of a consumer or business application 
• Hands-on technical capability: willing to write code along with the team you’re managing; Python and SQL experience preferred 
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects. 
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams. 
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing 

•  Work directly with the world’s leading AI labs and enterprises at the cutting edge of RL environment design and post-training. 
• Real impact: your environments will be used to evaluate and train frontier models on GDP-moving tasks across real-world domains 
• Talent-dense team, where you'll find high autonomy, rapid iteration, and rapid learning curve

Compensation: $250,000 to $350,000 OTE + Equity

What we do

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications.

The Role

We are looking for a Principal Research Engineer – RL to own the end-to-end lifecycle of RL environment projects, spanning environment design, task generation, reward/verifier design, quality, and delivery to frontier AI labs and enterprise clients. 

This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and build RL environments specific to their needs. You will lead teams of engineers, subject matter experts (e.g. Finance expert, if you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this.

What You’ll Do 

• End-to-End Ownership: Lead RL Environment projects end-to-end for one or more clients, ensuring the environment you and your team create matches the client’s spec, surpasses quality expectations, and is delivered on time. 
• Data Quality: Ensure the RL environments you produce, the data that goes into those environments, and the the data generated from them (e.g. agent trajectories and reward scores) meet frontier standards for realism, difficulty, diversity. 
• Team building and enablement: Work with your Ops counterparts to build the team of full-stack engineers, back-end engineers, domain experts, QAs, data creators, reviewers, and others you’ll need to deliver the environment on time. You’ll interview, hire, onboard, train, retain talent for your team 
• Process Leadership: Set the process that each of the above team members follows to generate environment code, database schemas, seed data, tasks, and verifiers; set up quality rubrics, automated validation scripts, and human-in-the-loop review processes for every aspect of the environment and data for the environment.
• Customer Interaction: Own customer relationships for your RL Environment project(s), and act as the primary point of contact for leading AI labs, providing regular updates, asking for feedback, and identifying opportunities to grow project scope and revenue. 
• Sales & Solutioning: participate in client solutioning conversations alongside our sales teammates; understand the needs of researchers at AI labs, translate those needs into environment goals 
• Evals & Post-training: Demonstrate proof of value for your environments by running inhouse RL fine tuning experiments to measure model performance lifts on agent trajectories; or by producing eval reports of frontier models on your environment and tasks

Who We’re Looking For

• RL & Post-training experience: familiarity with RL fine tuning, verifier/reward design, and/or environment design 
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.
• Systems thinking + Database/API design: ability to ‘simulate’ the data schema and API interface of a consumer or business application 
• Hands-on technical capability: willing to write code along with the team you’re managing; Python and SQL experience preferred 
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects. 
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams. 
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing 

•  Work directly with the world’s leading AI labs and enterprises at the cutting edge of RL environment design and post-training. 
• Real impact: your environments will be used to evaluate and train frontier models on GDP-moving tasks across real-world domains 
• Talent-dense team, where you'll find high autonomy, rapid iteration, and rapid learning curve

Compensation: $250,000 to $350,000 OTE + Equity

The Role

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications

The Staff Research Engineer – Code will own end-to-end the creation of datasets, RL environments, and evals for frontier AI labs in the domain of coding agents and software engineering. This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and interface directly with researchers / engineers at those labs to understand their needs and build data offerings to address those needs. To achieve this, you will build and manage teams of software engineers, researchers, QAs, and contractors/data-annotators from Turing’s talent pool of 4M+ developers.  

You’ll be responsible for delivering projects at frontier quality and scale—owning data quality, throughput, and timely delivery. You’ll define and manage data pipelines, validation workflows, and review processes to ensure datasets meet the highest standards for realism, correctness, and diversity. You’ll also develop automations, synthetic data generation systems, and internal tools to scale production efficiently. 

In short, you’ll run your project like a startup within Turing, owning both the technical architecture and the operational execution required to produce best-in-class datasets/environments/evals to make the world’s best coding agents and models even better at real-world coding tasks across the software development lifecycle.

What you’ll do

1. End-to-End Ownership: Data Quality, Process Design, and Team Building

• Lead the creation of datasets, rl environments, and evals focused on Coding Agents / Software Engineering for one or more AI lab customers.
• Ensure that everything you ship to clients meets frontier standards for realism, correctness, diversity, and difficulty.
• Set up quality rubrics, automated validation scripts, and human review processes for every stage of data generation.
• Build and lead cross-functional teams of software engineers, researchers, QAs, and data creators drawn from Turing’s 4M+ developer network.
• Interview, onboard, train, and mentor team members to ensure consistent output quality and technical excellence.

2. Collaborate with Researchers at Frontier Labs

• Act as the primary technical point of contact for your customer projects, interfacing directly with researchers and engineers at frontier AI labs to understand their coding agent roadmap and model data needs, to gather feedback, and to co-define success criteria for your projects.
• Provide regular progress updates, surface insights from model evaluations, and incorporate client feedback to improve future iterations.

3. Drive Research, Sales Enablement, and Industry Thought Leadership

• Fine-tune models in-house on Turing-generated datasets or Turing-rl-environment generated trajectories to determine model improvement as a proof of data quality
• Proactively build benchmarks and run evals on frontier models and coding agents to identify strengths and weaknesses on SWE tasks, and leverage these insights to inform product roadmap
• Equip customer-facing teams with the Evaluation reports, sample datasets, and trainings to enable them to communicate your data offerings to customers most effectively
• Publish research papers and technical posts on Turing’s data products, innovations in our synthetic data generation / automation pipelines, evaluations of frontier agents and models, and Turing’s model fine-tuning results on our datasets.

4. Build Tools and Infrastructure

• Oversee development of internal tools that accelerate data generation and verification (e.g., automated data scraping pipelines, unit test generators, repo sandboxing).
• Design dashboards and APIs for customers to run model evals, view performance reports, and integrate Turing data directly into their post-training pipelines.

What we’re looking for

• Post-training experience on SWE tasks or experience building coding agents: We expect that you have a deep understanding of data ingredients and design principles that lead to measurable coding model improvements, either from fine-tuning models to improve SWE capabilities or building your own coding agents to improve upon SWE capabilities of the base model.
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.Hands-on technical capability: Fluency in Python and proficiency in one or more major languages (C++, Java, Go, Rust, or JS).
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects.
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams.
• Background in Computer Science, Machine Learning, or related technical field preferred.

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. Coding is the core reasoning substrate of intelligence—advancing models’ ability to understand, design, and write code is effectively advancing their capacity for logic, planning, and abstract thought.
• Real Impact (GDP): automating software engineering unlocks one of the largest productivity frontiers in history. The software engineering market represents trillions in global GDP, and every percentage gain in automation translates to profound efficiency and innovation benefits across all industries.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

Compensation: $250,000 to $350,000 OTE + Equity

The Role

We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications

The Frontier Data Lead – Code will own end-to-end the creation of datasets, RL environments, and evals for frontier AI labs in the domain of coding agents and software engineering. This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and interface directly with researchers / engineers at those labs to understand their needs and build data offerings to address those needs. To achieve this, you will build and manage teams of software engineers, researchers, QAs, and contractors/data-annotators from Turing’s talent pool of 4M+ developers.  

You’ll be responsible for delivering projects at frontier quality and scale—owning data quality, throughput, and timely delivery. You’ll define and manage data pipelines, validation workflows, and review processes to ensure datasets meet the highest standards for realism, correctness, and diversity. You’ll also develop automations, synthetic data generation systems, and internal tools to scale production efficiently. 

In short, you’ll run your project like a startup within Turing, owning both the technical architecture and the operational execution required to produce best-in-class datasets/environments/evals to make the world’s best coding agents and models even better at real-world coding tasks across the software development lifecycle.

What you’ll do

1. End-to-End Ownership: Data Quality, Process Design, and Team Building

• Lead the creation of datasets, rl environments, and evals focused on Coding Agents / Software Engineering for one or more AI lab customers.
• Ensure that everything you ship to clients meets frontier standards for realism, correctness, diversity, and difficulty.
• Set up quality rubrics, automated validation scripts, and human review processes for every stage of data generation.
• Build and lead cross-functional teams of software engineers, researchers, QAs, and data creators drawn from Turing’s 4M+ developer network.
• Interview, onboard, train, and mentor team members to ensure consistent output quality and technical excellence.

2. Collaborate with Researchers at Frontier Labs

• Act as the primary technical point of contact for your customer projects, interfacing directly with researchers and engineers at frontier AI labs to understand their coding agent roadmap and model data needs, to gather feedback, and to co-define success criteria for your projects.
• Provide regular progress updates, surface insights from model evaluations, and incorporate client feedback to improve future iterations.

3. Drive Research, Sales Enablement, and Industry Thought Leadership

• Fine-tune models in-house on Turing-generated datasets or Turing-rl-environment generated trajectories to determine model improvement as a proof of data quality
• Proactively build benchmarks and run evals on frontier models and coding agents to identify strengths and weaknesses on SWE tasks, and leverage these insights to inform product roadmap
• Equip customer-facing teams with the Evaluation reports, sample datasets, and trainings to enable them to communicate your data offerings to customers most effectively
• Publish research papers and technical posts on Turing’s data products, innovations in our synthetic data generation / automation pipelines, evaluations of frontier agents and models, and Turing’s model fine-tuning results on our datasets.

4. Build Tools and Infrastructure

• Oversee development of internal tools that accelerate data generation and verification (e.g., automated data scraping pipelines, unit test generators, repo sandboxing).
• Design dashboards and APIs for customers to run model evals, view performance reports, and integrate Turing data directly into their post-training pipelines.

What we’re looking for

• Post-training experience on SWE tasks or experience building coding agents: We expect that you have a deep understanding of data ingredients and design principles that lead to measurable coding model improvements, either from fine-tuning models to improve SWE capabilities or building your own coding agents to improve upon SWE capabilities of the base model.
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.Hands-on technical capability: Fluency in Python and proficiency in one or more major languages (C++, Java, Go, Rust, or JS).
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects.
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams.
• Background in Computer Science, Machine Learning, or related technical field preferred.

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. Coding is the core reasoning substrate of intelligence—advancing models’ ability to understand, design, and write code is effectively advancing their capacity for logic, planning, and abstract thought.
• Real Impact (GDP): automating software engineering unlocks one of the largest productivity frontiers in history. The software engineering market represents trillions in global GDP, and every percentage gain in automation translates to profound efficiency and innovation benefits across all industries.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

Compensation: $250,000 to $350,000 OTE + Equity

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

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.

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)
• 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).

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 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.