About the role
You'll be building the intelligent core of SetuAI — the recommendation systems and adaptive algorithms that make every student's learning path uniquely theirs. This is not a research role; it's applied ML with direct user impact.
You'll work with real data from 50,000+ students to improve our content recommendation engine, difficulty adaptation system, and predictive performance models. Results ship to production, not papers.
What you'll do
- Design and improve the adaptive learning algorithm that personalizes content for each student
- Build and iterate on recommendation models for question selection, topic ordering, and revision scheduling
- Analyse student performance data to find patterns and inform product decisions
- Collaborate with the product team to translate learning science principles into engineering features
- Set up ML pipelines, model evaluation frameworks, and A/B testing infrastructure
- Monitor model performance in production and respond to regressions
What you bring
- 3+ years of ML/AI engineering experience in a production environment
- Strong Python skills: pandas, scikit-learn, PyTorch or TensorFlow
- Experience with recommendation systems or collaborative filtering
- Comfortable with SQL and working with large structured datasets
- Solid understanding of ML evaluation metrics and A/B testing methodology
Nice to have
Not required — but these will make you stand out.
- Experience in EdTech, knowledge tracing, or cognitive science applications
- Familiarity with LLMs and their application in tutoring or Q&A systems
- Background in Item Response Theory (IRT) or Bayesian Knowledge Tracing
What we offer
- ★Competitive salary + ESOPs
- ★Work on ML problems with real educational impact at scale
- ★₹30,000/yr learning stipend
- ★Health insurance and wellness benefits
- ★Hybrid setup in Pune with flexible remote days
Our hiring process
Fast, transparent, and respectful of your time.
01
2 days
Application Review
02
30 min
Initial Assesment Call
03
2–3 days
Skills Assessment
04
1 hour
Final Interview