Invisible is an Applied AI research company closing the gap between frontier models and the enterprise. We provide the platform infrastructure that makes AI work in production at scale: unify fragmented data, map complex processes, orchestrate and evaluate agents, and stand up hyper-personalized workflow applications fast for companies like Grubhub, Alliance Global, and Thomson Reuters. Where models fall short, we build RL environments and data to fix them. The result: a virtuous cycle between enterprise deployment and model training.
Recognized as Best Predictive Analytics Platform of the Year at the 2026 AI Breakthrough Awards, Invisible makes AI work.
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
- 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
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.
The annual salary range for this position, depending on level, is: $180,000 - $233,000 USD
Bonus and equity is included in all full-time, non commission 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.
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.
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.
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.
