Role overview
We're building AI systems that have natural and behaviorally realistic real-time conversations with people. As an AI Engineer on our team, you'll design and ship the production systems that power these experiences and own AI reliability, responsiveness, and quality.
Mursion's value comes from helping people measurably improve. That means the scores and assessments we provide have to mean something—they need to be valid, reliable, and useful for learning. You'll own the systems that turn conversation data into meaningful measurement of interpersonal skills.
You'll work at the intersection of engineering and measurement science—building scoring systems, designing evaluation frameworks, and shipping analytics features that help learners and organizations see real progress.
What you'll work on
- Build and ship scoring and assessment features and turn multimodal conversation data into meaningful measures of interpersonal skills
- Integrate scoring and assessment systems into content authoring and user-facing feedback systems
- Design and implement rubrics and evaluation frameworks that are valid, reliable, and fair
- Help build the infrastructure that connects raw simulation data to actionable scores and feedback
- Collaborate with product, research, and partners to ensure assessments actually support learning—not just measurement
- Iterate based on real data about what helps users improve
What we're looking for
You should have:
- Experience shipping software products that serve real users
- Strong programming skills in Python
- Comfort working with messy, multimodal data (audio, text, behavioral signals)
- The ability to think critically about what a score means and whether it's measuring what you think it's measuring
- Clear communication skills for working across engineering, product, and stakeholder teams
You don't need:
- Experience training models from scratch
- Expertise in every technology we use (we expect you to learn)
Depth in an adjacent domain is a genuine plus. We value experience in areas that connect to our work, including:
- Psychometrics, assessment design, or educational measurement
- I/O psychology or behavioral science
- Learning science, instructional design, or education technology
- Data engineering, analytics, or ML evaluation frameworks
- Fullstack development or data visualization
We've seen strong AI engineers come from many paths. What matters is your ability to ship, learn, and collaborate—not whether your background looks like a checklist.