Invisible Technologies

Title: Head of AI Training Research Location: Worldwide - Remote Job Description: About Invisible Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most. Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere. Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets. Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology. About the Role We are looking for a commercially-minded research leader to serve as Head of AI Training Research, responsible for delivering measurable client outcomes through applied research excellence. This role sits at the intersection of research and revenue — leading a team of applied researchers, forward deployment engineers (FDEs), and project leads who work in close coordination to ensure every client engagement is backed by rigorous methodology and drives tangible results. This is not a purely academic role. You will be accountable for the quality, scalability, and commercial impact of our training research practice. What You'll Do Leadership & Commercial Accountability - Lead and align a cross-functional pod of applied researchers, FDEs, and project leads around shared client outcome goals - Own the research practice's contribution to revenue — including supporting pre-sales, scoping engagements, and ensuring delivery quality that drives retention and expansion - Act as an executive sponsor on strategic accounts, providing research credibility and depth to client relationships - Build a team culture that is rigorous, fast-moving, and relentlessly focused on client impact Client Delivery & Applied Research - Define the applied research frameworks, workflows, and quality standards used across all client engagements - Ensure applied researchers are translating benchmarking, data, and RL environment work directly into client-specific training solutions - Partner with project leads to scope engagements accurately, manage research risk, and hit delivery milestones - Work with FDEs to ensure research outputs are deployable, integrated, and producing measurable model improvements in client environments Benchmarking - Oversee the development of benchmarking capabilities used to demonstrate value to clients — pre- and post-engagement performance comparisons, capability gap analyses, and regression tracking - Ensure benchmark design is tied to client-defined success metrics, not just internal research goals - Use benchmark outputs as a feedback loop to continuously improve delivery quality across engagements OTS Data & Data Strategy - Lead strategy around sourcing, filtering, and deploying off-the-shelf datasets in support of client training objectives - Build repeatable frameworks for data quality assessment that can be applied efficiently across diverse client use cases - Identify reusable data assets and pipelines across engagements to improve margins and delivery speed RL Environment Building - Oversee the development of RL environments that are purpose-built or adapted for client-specific task performance - Ensure environments are reproducible and portable across client deployments - Partner with FDEs and applied researchers to close the loop between environment design and real-world client outcomes What We're Looking For - 8+ years in applied ML or AI research, with at least 3 years leading research or technical delivery teams in a client-facing or revenue-generating context - Proven track record of translating research capabilities — benchmarking, data curation, or RL — into delivered client value - Experience working across applied researcher, engineering, and project management functions; comfortable orchestrating cross-functional pods toward a shared outcome - Strong commercial instincts — able to scope, price, and communicate research work in terms of client ROI - Deep familiarity with LLM training pipelines, including fine-tuning, RLHF/RLAIF, and evaluation methodology - Excellent executive communication skills; confident representing the research practice in client conversations and during pre-sales Nice to Have - Prior experience at an AI services firm, applied research consultancy, or enterprise AI product company - Familiarity with data licensing, synthetic data generation, or contamination detection in the context of client data - Experience building scalable delivery infrastructure (templates, tooling, benchmarks) that improves team output across engagements What’s In It For You Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity. *Offers will also include bonus + equity You can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living. Bonuses and equity are included in all offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process. What It's Like to Work at Invisible: At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI. We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in. Country Hiring Guidelines: Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Evaluating AI model performance across standardized qualitative and quantitative metrics. • Assessing the model's basic computer programming literacy and understanding of structured data within a support context. • Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Role-Play Scenario Execution: Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions. • Model Performance Auditing: Evaluating AI model performance across standardized qualitative and quantitative metrics. • Technical Metric Evaluation: Assessing the model's basic computer programming literacy. • Representative Dataset Generation: Contributing to the development of diverse, high-quality audio datasets.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Role-Play Scenario Execution: Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Model Performance Auditing: Evaluating AI model performance across standardized qualitative and quantitative metrics, focusing strictly on task completion accuracy, conversational naturalness, and audio comprehension. • Technical Metric Evaluation: Assessing the model's basic computer programming literacy, including its understanding of JSON structures, functions, methods, and ability to reason about structured data within a support context. • Representative Dataset Generation: Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Role-Play Scenario Execution: Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Model Performance Auditing: Evaluating AI model performance across standardized qualitative and quantitative metrics, focusing strictly on task completion accuracy, conversational naturalness, and audio comprehension. • Technical Metric Evaluation: Assessing the model's basic computer programming literacy, including its understanding of JSON structures, functions, methods, and ability to reason about structured data within a support context. • Representative Dataset Generation: Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. Expected deliverables include: • Role-Play Scenario Execution: Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Model Performance Auditing: Evaluating AI model performance across standardized qualitative and quantitative metrics, focusing strictly on task completion accuracy, conversational naturalness, and audio comprehension. • Technical Metric Evaluation: Assessing the model's basic computer programming literacy, including its understanding of JSON structures, functions, methods, and ability to reason about structured data within a support context. • Representative Dataset Generation: Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.

• Operate autonomously to design complex evaluation frameworks and provide structured training data. • Role-Play Scenario Execution: Creating and executing complex, role-play-based evaluation scenarios that simulate realistic customer service interactions across travel, finance, and technical support domains. • Model Performance Auditing: Evaluating AI model performance across standardized qualitative and quantitative metrics, focusing strictly on task completion accuracy, conversational naturalness, and audio comprehension. • Technical Metric Evaluation: Assessing the model's basic computer programming literacy, including its understanding of JSON structures, functions, methods, and ability to reason about structured data within a support context. • Representative Dataset Generation: Contributing to the development of diverse, high-quality audio datasets that accurately reflect real customer expectations for clarity, efficiency, and natural conversational flow.

• Operate autonomously to provide linguistic QA and develop alignment resources. • Data Annotation & Review: Reviewing, annotating, and testing AI outputs for grammatical accuracy, naturalness, and strict cultural context. • Quality Assurance & Vetting: Acting as a primary quality check during production to proactively identify and correct subtle cultural errors or awkward phrasing in Haitian Creole. • Resource Generation: Analyzing task quality trends and autonomously developing educational resources and feedback documentation to increase alignment between AI task outputs and campaign expectations. • Production & Vetting Support: Providing native-level language vetting during project scaling and offering specialized linguistic consultation throughout the production phase.

• Operate autonomously to provide linguistic QA and develop alignment resources. • Review, annotate, and test AI outputs for grammatical accuracy, naturalness, and strict cultural context. • Act as a primary quality check during production to proactively identify and correct subtle cultural errors or awkward phrasing. • Analyze task quality trends and autonomously develop educational resources and feedback documentation. • Provide native-level language vetting during project scaling and offer specialized linguistic consultation.

• Operate autonomously to provide linguistic QA and develop alignment resources. • Reviewing, annotating, and testing AI outputs for grammatical accuracy, naturalness, and strict cultural context. • Acting as a primary quality check during production to proactively identify and correct subtle cultural errors or awkward phrasing in the Arabic language. • Analyzing task quality trends and autonomously developing educational resources and feedback documentation to increase alignment between AI task outputs and campaign expectations. • Providing native-level language vetting during project scaling and offering specialized linguistic consultation throughout the production phase.