Tech Lead - Machine Learning
JOB TITLE: TECH LEAD - MACHINE LEARNING
LOCATION: MUMBAI/BENGALURU, INDIA
JioSaavn is South Asia’s leading audio streaming service to access, discover, and listen to favorite songs & Podcasts across languages and genres. We blend digital technology, data analysis (which we have affectionately coined Music Science), and a strong, fearless business acumen to reach all corners of the globe. Our award-winning mobile products, partnerships, innovations and thought leadership have been featured in some of the world’s leading publications, from The New York Times, to The Wall Street Journal, The Economic Times to Forbes, and many more.
At JioSaavn, we ignite passion and performance to work towards a collective goal: creating the perfect mobile entertainment ecosystem that delivers the best possible music experience to millions of listeners around the world. Our default mode is that of perpetual innovation. Together, we form a concerted rhythm that goes beyond borders. We don't just go with the flow, we create it.
JioSaavn offers a dynamic and unconventional work environment, full of fun wholesome experience. We believe creativity and technology blend together like sweet melodies. When you choose JioSaavn, you join a diverse world of high-calibre techies, artists, and inventors hailing from companies like Yahoo!, Twitter, LinkedIn, Google, Qualcomm, HBO, Microsoft, Flipkart, Amazon, Paytm, Quikr etc.
Our value-based, people-first work culture is about empowering every individual in our team to be catalysts for change in this dynamic digital world. Every day is an opportunity to bring your vision to life, and to expand, learn and grow. No idea is left unconsidered. No voice is left unheard.
JioSaavn prides itself on being an equal opportunity employer. We have committed ourselves to creating a safe environment with fair and equal access and opportunities, sans discrimination. We encourage everyone to be open to experiences and perspectives beyond their normal; divergent thinkers create differentiated products, and even better music.
If our vibe matches with yours, we'd love to hear from you.
You will be involved in applying modern machine learning to solve various product and business problems
including ML model lifecycle management with ideation, experimentation, implementation, and
maintenance. Your work would be impacting millions of users the way they consume music and podcasts,
it would involve solving cold start problems, optimizing ranking, and improving recommendations for
serving relevant content to users.
We are looking for a key individual contributor to drive our recommendations and discovery projects and
also be involved in tech management within the team by mentoring and guiding other folks in the team.
You will own product-impacting projects end-to-end. The small teams of talented, passionate people in
which you’ll work will include engineers and data scientists.
Who You Are:
- A machine learning software engineer with a passion for working on exciting, user impacting product and
- business problems
- Stay updated with recent advances in machine learning
- Have taken scalable ML services to production, maintained and managed their lifecycle
- Good understanding of foundational mathematics associated with machine learning such as statistics,
- linear algebra, optimization, probabilistic models
- 5+ years of industry experience doing Applied Machine Learning
- Fluent in one or more object-oriented languages like Python, C++, Java
- Knowledgeable about core CS concepts such as common data structures and algorithms
- Comfortable conducting design and code reviews
- Master’s or Ph.D. degree in Computer Science, Mathematics or related field
- Industry experience with large scale recommendation systems
- Hands-on experience with Spark, Hive, Flask, Tensorflow, XGBoost, Airflow
BENEFITS AND PERKS:
At JioSaavn, we blur work and play, and you get all the perks of a global company. You will get to work with a dynamic group of entrepreneurs, who are delivering results and working zealously to make a difference in the way the world experiences music.