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 Invisible is building a reinforcement learning capability inside its research organization, focused on evaluation methodology, benchmarks, and RL environments for frontier labs and enterprise clients. As a Research Engineer, you'll work on how frontier models are measured, meaning the evaluations that labs and enterprises actually make decisions on, with direct influence over the methodology and not just its implementation. You'll take a research question (how do we measure whether a model can do this work, and how do we make that measurement reproducible?) and turn it into a scoring framework, an evaluation architecture, or an environment design, then ship the production system that runs it. This role doesn't hand specifications to someone else to build, and it doesn't only build what others have specified. It's a fit for someone who wants to design the approach and write the code that proves it out. Depending on level, the role reports to the Lead Research Engineer or, at Principal, directly to the VP of Research. What You'll Do Design benchmarks and RL environments that measure real model capability for frontier labs and enterprise clients Originate evaluation methodology and translate it into scoring frameworks, rubrics, and evaluation architectures Write and ship the production code that implements your designs Build and maintain the data pipelines that feed evaluation runs Run the analysis that establishes whether a result holds, and make evaluations reproducible Partner with Research Scientists on methodology review, with Solutions Architects on client requirements, and with ML software engineers who build and maintain the underlying platform What We Need Production-quality code written daily; this is a hard requirement Python & ML Stack: Fluency in Python and comfort across the modern ML stack Evaluation & Infrastructure: Real experience building evaluation systems, RL environments, or training and inference infrastructure Agentic Systems: Hands-on experience with modern agentic flows RL & Frontier Evaluation: Familiarity with reinforcement learning methods and how frontier models are evaluated; we weigh this more heavily than years of experience A track record of turning ambiguous research questions into working systems, and publishing or shipping the result 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 full-time 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. AI Interviewing Guidelines: Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey. Click here to learn more about how we use AI and our guidelines for candidates. Accessibility Statement: We are committed to providing reasonable accommodations for individuals with disabilities. If you require an accommodation to participate in the application or interview process, please submit your request using our accommodation request form. A member of our team will follow up to support your request. Here is the link the google form: docs.google.com Equal Opportunity Statement: We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law. Due to a high volume of candidates, Invisible may use automated decision-maker technologies to filter candidates based on response to our application questions and other provided information. Our use of automated decision-making enables us to be efficient by providing a manageable list of possible candidates that meet our mandatory hiring criteria. If you object to our use of automated decision-making please contact us.

Requirements

Must-haves

Design benchmarks and RL environments that measure real model capability for frontier labs and enterprise clients
Originate evaluation methodology and translate it into scoring frameworks, rubrics, and evaluation architectures
Write and ship the production code that implements your designs
Build and maintain the data pipelines that feed evaluation runs
Run the analysis that establishes whether a result holds, and make evaluations reproducible
Partner with Research Scientists on methodology review, with Solutions Architects on client requirements, and with ML software engineers who build and maintain the underlying platform
What We Need
Production-quality code written daily; this is a hard requirement
Python & ML Stack: Fluency in Python and comfort across the modern ML stack
Evaluation & Infrastructure: Real experience building evaluation systems, RL environments, or training and inference infrastructure
Agentic Systems: Hands-on experience with modern agentic flows
RL & Frontier Evaluation: Familiarity with reinforcement learning methods and how frontier models are evaluated; we weigh this more heavily than years of experience
A track record of turning ambiguous research questions into working systems, and publishing or shipping the result

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsmlpythonaccessibility

Platform: greenhouse
Effort: quick