Overview
What this challenge is about.
Tune a Pick-and-Place Controller for a Cosmetics Co-Packer. Beginner-friendly challenge in code. Writing production code that solves real engineering problem...
The Brief
What you'll do, and what you'll demonstrate.
Halve the missed-pick rate on a cosmetics co-packing line without sacrificing cycle time.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Tune a real robot controller's motion profile against measured outcomes
- Run a structured parameter search without overfitting to training data
- Validate controller changes via offline replay
- Communicate engineering changes to a non-engineer line lead
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Motion Control
Apply motion control to solve real industry problems and demonstrate production-level capability.
- Trajectory Tuning
Apply trajectory tuning to solve real industry problems and demonstrate production-level capability.
- Robot Kinematics
Apply robot kinematics to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Yaml Configuration
Apply yaml configuration to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Tuning a real robot controller against production logs is everyday AI-engineer work in manufacturing-robotics teams; this challenge gives the student a concrete shipped change to point at.
This challenge sharpens
- motion-control
- python
- evaluation
Machine Learning Engineer
Structured search with held-out validation and Pareto reporting is the same discipline MLEs use when tuning models or pipelines.
This challenge sharpens
- trajectory-tuning
- evaluation
- python
Applied AI Scientist
Translating an engineering change into a line-lead memo is the applied-AI scientist's communication muscle at industrial-robotics companies.
This challenge sharpens
- motion-control
- evaluation
- trajectory-tuning