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Invisible Technologies is building a reinforcement learning capability within its research organization, focused on evaluation methodology, benchmarks, and RL environments for frontier labs and enterprise clients. The company's end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, and integrates human expertise. Invisible is profitable, reached $134M in revenue, ranked #2 fastest-growing AI company on the 2024 Inc. 5000, and recently raised $100M in growth capital.
As a Research Engineer, you will work on how frontier models are measured—the evaluations that labs and enterprises actually make decisions on—with direct influence over methodology, not just implementation. You will take research questions (e.g., "How do we measure whether a model can do this work, and how do we make that measurement reproducible?") and turn them into scoring frameworks, evaluation architectures, or environment designs, then ship the production system that runs it. This role requires both design thinking and hands-on coding; you will not hand specifications to others, nor will you only build what others have specified.
Key responsibilities include: designing benchmarks and RL environments that measure real model capability for frontier labs and enterprise clients; originating evaluation methodology and translating it into scoring frameworks, rubrics, and evaluation architectures; writing and shipping production code that implements your designs; building and maintaining data pipelines that feed evaluation runs; running analysis to establish whether results hold and making evaluations reproducible; and partnering with Research Scientists on methodology review, Solutions Architects on client requirements, and ML software engineers on platform maintenance.
Depending on level, the role reports to the Lead Research Engineer or, at Principal level, directly to the VP of Research.
REQUIREMENTS:
- Production-quality code written daily (hard requirement)
- Fluency in Python and comfort across the modern ML stack
- Real experience building evaluation systems, RL environments, or training and inference infrastructure
- Hands-on experience with modern agentic flows
- Familiarity with reinforcement learning methods and how frontier models are evaluated (weighted more heavily than years of experience)
- Track record of turning ambiguous research questions into working systems, and publishing or shipping the result
