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

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

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

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: enterprise knowledge work (EKW)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Reports to: CRO (until VP, Field Engineering is hired)

Compensation: OTE $260–320K (Senior) or $325–400K (Staff) · 75/25 base/variable split · Equity

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the enterprise knowledge work domain. Every major lab is racing to push the frontier on multi-step reasoning over enterprise data, tool use, long-horizon task completion, and evaluation that reflects real work. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

This is a founding Field Engineering role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

What You'll Do

1) Technical discovery — lead the technical track on every qualified EKW opportunity

• Partner with Research Partners to run the technical conversation with lab researchers and engineers.
• Understand what agentic capability the lab is trying to unlock, what "good" looks like, and what evaluations a post-training team would actually trust.
• Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.

2) Solution architecture — translate capability goals into scoped Turing deliverables

• Map research goals to Turing's offering shapes: agentic trajectories, rubric-graded reasoning tasks, tool-use evaluations, and domain-specialist-built datasets.
• Author technical proposals that frontier lab research leads accept and the Production Engineering team can execute without a rewrite.

3) Prototyping and demo-building — prove the approach before contract

• Build reference agent loops, sample multi-step evaluations, and graded trajectories that demonstrate quality before contract signature.
• The demo has to run. Expect to write real code.

4) POC ownership — take paid pilots from kick-off to scale-up decision

• Design a measurement plan the lab's research team will actually read and act on.
• Define success criteria, own the cadence, convert POC to production contract.

5) R&D interface — channel GTM-to-R&D asks for Enterprise Knowledge Workflow opportunities

• Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
• Maintain a collaboration cadence that R&D teams trust.

6) Playbook building — codify what works so future hires scale faster than you did

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns for EKW opportunities.
• Own the EKW section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in applied AI, data engineering, or ML engineering, with meaningful work on agentic systems, RAG, tool use, or enterprise-knowledge LLM applications.
• Strong Python fluency and production experience with LLM orchestration frameworks (LangGraph, LlamaIndex, DSPy, or equivalents).
• Experience designing evaluations for multi-step reasoning or agentic systems — rubric design, trajectory grading, measurement beyond single-turn accuracy.
• Exposure to complex enterprise workflows (financial services, life sciences, legal, or similar) and the data and permission realities inside them.
• A high written communication bar: you can produce a scoping document that a frontier lab research lead accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, an AI startup building agentic products, or an enterprise AI team shipping to production.
• Experience with agentic or reasoning benchmarks (e.g., GAIA, τ-bench, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; enterprise knowledge work domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on EKW opportunities you cover is materially above the non-covered baseline; qualification bar codified.
• 180 days: a second Pre-Sales AI Solutions Engineer in the EKW domain hired behind you, ramping off your playbook.

Why Turing

• Work directly with the world's leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
• Real impact on the path to AGI: the datasets, evaluations, and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards, not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI, not to procurement.

How to apply

Send a resume or CV and a short note on a technical artifact you built — ideally something customer-facing, evaluation-adjacent, or that demonstrates how you think about technical scoping. We read every submission.

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: Software Engineering (SWE)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Reports to: CRO (until VP, Field Engineering is hired)

Compensation: OTE $260–320K (Senior) or $325–400K (Staff) · 75/25 base/variable split · Equity

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the software engineering domain. Every major lab is racing to push the frontier on code generation, agentic software engineering, and SWE evaluation. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

This is a Field Engineering founding role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

What You'll Do

1) Technical discovery — lead the technical conversation on every qualified SWE opportunity

• Partner with Research Partners to run the technical track with AI researchers and research leads.
• Understand what they're training, what they're evaluating, where their pipeline breaks, and what a Turing-built artifact looks like in practice.
• Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.

2) Solution architecture — translate lab needs into scoped Turing deliverables

• Map capability goals to Turing's offering shapes: custom human expert data, off-the-shelf datasets, and managed talent.
• Author technical proposals that AI researchers accept and the Production Engineering team can execute without a rewrite.

3) Prototyping and demo-building — prove the approach before contract

• Build sample eval tasks, reference dataset slices, graded trajectories, and working agentic scaffolds.
• Expect to write real code, not mock-ups. The demo has to run.

4) POC ownership — take paid pilots from kick-off to scale-up decision

• Design the measurement plan, define success criteria, own the cadence.
• The outcome you are measured on: POC converts to production contract.

5) R&D interface — be the pre-sales channel between GTM and R&D for SWE

• Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
• Maintain a predictable collaboration cadence that R&D teams trust.

6) Playbook building — codify what works so future hires scale faster than you did

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns.
• Own the SWE section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in software or ML engineering, with meaningful production experience on code-generation, code-understanding, or developer-tooling systems.
• Hands-on fluency in Python and modern LLM tooling; comfort reading and writing across at least one other major language (TypeScript, Go, Rust, or Java).
• Experience designing or working with evaluations for code models — benchmarks, rubric design, grader reliability, eval construction.
• Experience with large codebases, agentic SWE systems, or developer-facing AI products.
• A high written communication bar: you can produce a scoping document that a frontier lab engineer accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, AI infrastructure company, or developer-tools company shipping to AI customers.
• Experience with SWE benchmark construction (e.g., SWE-bench, LiveCodeBench, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; software engineering domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on SWE opportunities you cover is materially above the non-covered baseline; qualification bar codified; R&D and Field Engineering interface running on a predictable cadence.
• 180 days: a second Pre-Sales AI Solutions Engineer in the SWE domain hired behind you, ramping off your playbook. You spend less than 30% of your time as a solo contributor and more than 70% multiplying through the function.
  
  

Why Turing

• Work directly with the world's leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
• Real impact on the path to AGI: the datasets, evaluations, and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards, not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI, not to procurement.

How to apply

Send a Resume or CV and a short note on a technical artifact you built — ideally something customer-facing, evaluation-adjacent, or that demonstrates how you think about technical scoping. We read every submission.

 Position Overview

We are seeking a Strategic Project Lead to own end-to-end delivery and growth for audio and multimodal AI initiatives. This is a client-facing, builder role at the forefront of AI — spanning audio, speech, language, and multimodal systems. The scope includes both commercial and technical ownership. You’ll identify opportunities for collaboration, define the structure and terms of each engagement, and ensure successful delivery across internal and external teams.

Success in this role requires the ability to navigate early-stage problem spaces, build trust with technical stakeholders, and consistently deliver outcomes in complex, evolving environments.

We are looking for a strategic thinker with strong Audio AI data annotation experience who can translate loosely defined technical objectives into executable programs and grow relationships by consistently delivering value in complex, evolving environments.

Primary Responsibilities

1. Delivery Ownership & Client Partnership

• Own P&L, delivery outcomes, and client satisfaction for audio and multimodal engagements
• Lead client conversations and solutioning, translating ambiguous needs into structured programs
• Build strong relationships with enterprise and frontier AI lab partners
• Identify and drive account expansion opportunities, shaping early-stage ideas into commercial engagements

2. Delivery Systems & Scalable Execution

• Design and run high-performance delivery systems across audio and multimodal workflows
• Define metrics and leading indicators to proactively manage quality, speed, and cost
• Build reusable playbooks and frameworks to accelerate program launches
• Scale and structure global delivery teams, ensuring clear ownership and fast decision-making

3. Cross-Functional Influence & Strategic Alignment

• Align product, engineering, and operations around client priorities
• Drive execution in ambiguous, fast-changing environments
• Feed client and delivery insights into internal roadmap and capability building

4. Technical & Domain Leadership (Audio / Multimodal)

• Own and evolve Turing’s audio and speech capability across ASR, TTS, conversational audio, and multimodal systems
• Build new audio offerings — including data generation, evaluation frameworks, alignment workflows, and emerging agentic/audio use cases
• Partner with ML/research teams to define benchmarks and evaluation methodologies
• Translate advances in LLMs, speech, and multimodal AI into scalable, client-ready solutions

Requirements

Core Experience

• Demonstrated experience in delivery management, program management, or technical operations — with exposure to AI/ML or data workflows
• Domain background and experience in audio/speech AI, ASR/TTS, NLP, or multimodal data annotation
• Familiarity with frontier AI lab workflows: model evaluation, RLHF, alignment, benchmarking. Knowledge of the latest multimodal AI technologies and how they can improve annotation pipelines

Problem Solving & Ownership

• Strong first-principles problem-solving ability; comfortable operating with ambiguity
• Proven ability to own and scale complex programs end-to-end
• High ownership mindset with bias for action

Communication & Execution

• Strong communication skills; able to engage technical and business stakeholders
• Ability to make trade-offs across quality, speed, and cost
• Strong analytical skills with experience using data to drive decisions

Other

• Comfortable working across global teams and time zones
• Location : Based out of US
• Salary: $200k - $250k 

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

Compensation

232k-260k base + variable + equity