Product before ceremony
We prefer direct problem solving, strong writing, and shipped improvements over process theater.
Build the operating system for AI search visibility.
Chatobserver measures how brands appear inside AI search — across prompts, citations, and positioning signals. We build the research systems, product surfaces, and data infrastructure that turn raw model output into something operators can actually use.
Open roles
14 total
Research3
Engineering4
Product2
Growth3
Operations2
Open roles
Fourteen roles across research, engineering, product, growth, and operations. All of them are close to the product, close to the real data, and close to the quality bar that will define the company.
Research
AI Researcher
Research and product systems
Soon
→Research Engineer
Research infrastructure
Soon
→Evaluation Specialist
Research and product systems
Soon
→Engineering
Member of Technical Staff
Product engineering
Soon
→Senior Software Engineer
Product engineering
Soon
→Data Platform Engineer
Infrastructure and data
Soon
→Frontend Engineer
Product engineering
Soon
→What to expect
We keep the process focused. The goal is to understand how you think, what you have built, and how you would raise the quality bar here.
1
Apply
Send us your background, relevant work, and why this role makes sense for you.
2
Foundational conversation
A focused conversation about your work, your judgment, and the role itself.
3
Role-specific deep dive
A discussion or exercise that looks like the actual work more than a generic interview loop.
4
Founder conversation
A final conversation on standards, ambition, and what success would look like here.
5
Decision
We close the loop clearly and move quickly once there is conviction.
How we work
The team should feel small, direct, and serious. We care about speed, but not speed that leaves a mess for someone else.
Research and implementation stay close
The people asking hard questions about AI behavior should stay close to the systems that operationalize those answers.
Quality compounds
We want code, language, and product decisions that stay understandable under change.
Operators deserve clarity
The output must help teams explain what changed, why it matters, and what to do next.
If this feels like the right problem, keep reading.
The role pages go deeper on the work, the standards, and what a strong fit actually looks like.
Questions? Email [email protected]