Staff Machine Learning Engineer - PyTorch/Tensorflow

Within the ShareChat AI team, we are hiring an experienced Staff Machine Learning Engineer to drive the development of cutting-edge ad prediction models. This IC role is central to our Ads team and will focus on end-to-end ownership of core ML modeling - training, deploying, and improving models that predict key user actions such as clicks, installs, and deeper funnel events like transactions.

Beyond this, there are exciting opportunities to deploy ML in the ADs ecosystem, like on the network ADs side of things, as well as on balancing ad revenue with regression in user experience. This is a high-impact role with direct revenue implications, where experimentation success rates are significantly higher due to our dynamic and fast-paced environment. You would be joining us at an exciting time! The science behind recommendation systems is rapidly changing, and we're making big progress at a rapid pace.

Responsibilities :

- Design and help develop systems that serve recommendations to over 300 million users.

- Drive ML roadmap creation and execution, specifically around Ads.

- Provide technical guidance in ML model formulation, implementation & experimentation, and take end-to end ownership of ML systems and key user satisfaction-based metrics.

- Drive architectural strategy and design for complex ML systems that support the needs of users, creators, and content stakeholders.

Requirements :

- 8+ years of hands-on experience training and serving large-scale models using frameworks such as Tensorflow or PyTorch.

- Experience in productionising machine learning models, managing and designing end-to-end ML systems, and data pipelines.

- Deep understanding of the mathematical foundations of Machine Learning algorithms.

- Direct experience in building and applying large-scale (100M+ users) machine learning solutions for feed ranking and personalizing recommendations.

- You stay up-to-date with the state-of-the-art technology in the domains of recommender systems, data engineering, and machine learning.

- Relevant publications in top-tier applied machine learning conferences are a plus.