Company Description
byteXL is an edtech company dedicated to transforming engineering education in India by bridging the gap between academia and industry. Through strategic partnerships with colleges and industry experts, byteXL integrates curriculum, practical learning, and content tailored to impart critical employability skills. The company is committed to equipping students with the tools required to excel in their careers and contribute to the nation's development. byteXL envisions a future where every engineering student receives holistic, industry-relevant education that enhances their skills and career prospects.
Role Summary
As a Content Lead for Machine Learning, you'll own the entire learning journey from probability and statistics through classical ML to deep learning and NLP. That includes designing the curriculum, writing reading materials, building assessments, creating lab exercises, and developing projects that teach students how to actually build and deploy models, not just understand them theoretically.
This is a hands-on creation role. You'll spend most of your time building, testing, and improving content. You'll also be running experiments and building ML projects yourself regularly, because the content needs to come from someone who's done the work, not just read about it.
If you're someone who can derive a loss function from scratch, train a transformer on a custom dataset, and then write about both in a way that makes it click for someone seeing it for the first time, this is your role.
What You'll Do
- Design semester-wise curriculum and decide how topics are structured, sequenced, and paced across the academic calendar.
- Continuously evolve the content strategy based on what's working, what's not, and where the ML ecosystem is heading.
- Write clear, in-depth reading materials that students can actually learn from independently. Not surface-level overviews, but explanations that go into the math, the intuition, and the implementation.
- Design problems that test whether students actually understand what's happening under the hood, not just whether they can call `model.fit()`.
- Build question banks following ByteXL's quality standards for difficulty distribution, answer balance, and auto-gradability.
- Create hands-on lab exercises that take students from cleaning a dataset to evaluating a trained model.
- Build ML projects yourself regularly to validate that what you're asking students to do is actually doable, meaningful, and teaches the right lessons.
- Stay current with how the data science and ML space is evolving.
- Try new ways of teaching and building content. If something could work better, prototype it and make the case.
What We're Looking For
Must Have
- B.Tech, M.Tech, MCA, or equivalent in Computer Science, Mathematics, Statistics, or a related field.
- 35 years of experience in applied machine learning, data science, or content development with a strong focus on ML.
- Expert-level Python proficiency with additional experience in at least one other programming language.
- Strong mathematical foundations in linear algebra, calculus, probability, and statistics.
- Proficiency in exploratory data analysis, data visualization, and statistical methods.
- Proficient with core data science tooling like pandas, NumPy, scikit-learn, and matplotlib/seaborn.
- Working knowledge of deep learning architectures including CNNs, LSTMs, Transformers, and diffusion models.
- Solid understanding of modern NLP concepts such as tokenization, embeddings, attention mechanisms, and large language model fine-tuning.
- Has built and deployed models in production, not just in notebooks.
- Knows how to build MVPs using tools like Streamlit, Gradio, or similar.
- Can explain a backpropagation pass or a Bayesian prior clearly enough that someone seeing it for the first time walks away understanding it.
Nice to Have
- Hands-on experience with Edge AI and Explainable AI (XAI).
- Experience with experiment tracking and MLOps tools like MLflow or Weights & Biases.
- Published research papers in conferences or journals.
- Has competed on platforms like Kaggle, MachineHack, or Zindi.
- Has curated and built their own datasets.
- Background in academic teaching or developer education.
