About the Company
Scaler School of Technology School of Technology (SST) is a 4-year, full time residential undergraduate program in Computer Science & AI. With a sub-4% acceptance rate, SST is building Indiaʼs Ivy League for tech
- where elite engineering meets real-world outcomes, mentorship, and hands-on experience. In just its third year, over 22,000 students applied for only a few hundred seats, with many leaving offers from NITs and BITS to join SST. The campus today is home to a handpicked cohort of students who are not only academically outstanding but also driven to build startups, publish research, and compete in global contests. The environment is intentionally designed to mirror the worldʼs best technical universities
- selective intake, high-caliber peer group, and access to industry leaders.

About the Role
Job Title: Teaching Assistant (M.
Tech) NLP and Transformers

Location:
Electronic City, Bangalore Work Mode: On-Site, part-time (5–6 hours per week) We are looking for M.
Tech Teaching Assistants for NLP and Transformers, a 4-credit, 7-week course in the B.
Tech AI/ML track (Year 3). The course is hands-on: 70% of the grade comes from 6 labs, 2 assignments and a team capstone, where students build tokenizers, a mini-GPT from scratch, and fine-tuned LLMs. Your main job is to help students get unstuck during the 2-hour labs, so they finish what they started. Outside the lab, you will help evaluate student work and support the instructor with course operations. Responsibilities What You Will Do / Responsibilities Lab Support (main responsibility)
• Attend 2 labs a week, one for each student batch (2 hours each),



and move around the room helping students debug code, environment and GPU issues.
• Guide students towards the answer with hints and questions, rather than writing their code for them.
• Spot common mistakes during a lab and flag them to the instructor, so they can be addressed for the whole class.
• Note which students are falling behind and help them catch up. Lab Preparation
• Work through the lab notebook yourself a few days in advance, test it on Colab and Kaggle, and report bugs or unclear steps.
• Help prepare setup guides, starter code and FAQs for common errors. Evaluation
• Grade lab submissions and assignments against published rubrics, with short written feedback.
• Help review capstone proposals and milestones, and sit on demo-day panels if needed.
• Support the 10-minute vivas held for a random sample of students on each assignment.
• Flag suspected plagiarism or undisclosed AI-tool use to the instructor. Course Support
• Answer student questions on the course forum or chat within an agreed response time.
• Hold optional doubt-clearing hours when assignments are due.
• Help with other course tasks as needed, such as tracking attendance and grades, preparing datasets, or reviewing exam questions. Engagement Details




• Duration: 7 weeks of teaching, plus 1 to 2 weeks for final evaluation.
• Labs: 2 labs a week (one per batch), 2 hours each, in person.
• Weekly load: 5 to 6 hours, covering labs, preparation, grading and student questions. Why This Role
• Current, hands-on NLP: Work with Transformers from scratch, fine-tuning with LoRA and QLoRA, and LLM evaluation.
• Teaching

experience:
Build mentoring and teaching experience that strengthens a PhD, research or industry application. Qualifications Required Qualifications Education
• Currently pursuing or recently completed an M.
Tech in Computer Science, AI, Data Science or a related field. Required Skills
• Knowledge, Skills, and Abilities
• Python: comfortable reading and debugging other peopleʼs code quickly.
• Deep learning: working knowledge of PyTorch, including writing and debugging a training loop.
• NLP fundamentals: tokenization, word embeddings, RNNs and the attention mechanism in Transformers.
• Tools: hands-on experience with Hugging Face (Transformers, Datasets) and Jupyter, Colab or Kaggle notebooks.
• Communication: able to explain a concept simply and patiently in English. Preferred Skills
• Good to Have
• Has fine-tuned a pretrained model, ideally with LoRA or QLoRA.
• Familiar with evaluating language models: metrics, LLM-as-judge, bias checks.
• Has been a TA, tutor or mentor before.
• Has an active GitHub profile, a Kaggle profile or a published paper in NLP. Pay range and compensation package 8,500 to 10,000 per month (fixed)

📌 Teaching Assistant (Bengaluru)
🏢 Scaler
📍 Bengaluru