I
InMobi
Applied Scientist (User Intelligence)
Skills
Deep LearningData SciencePyTorchMachine Learning
25 days ago
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Important information
Contract type:
Permanent
Salary:
$182,250 – $218,500 / year
Location:
United States
Work mode:
Onsite
Published on:
27 September 2026
What they need
- We are looking for an Applied Scientist to develop and improve our user personalization intelligence through architectural and data research aimed at optimizing the efficacy of our targeting and pricing systems
- More specifically, our team is exploring state-of-the-art attention-based architectures powered by multi-modal data, operating at InMobi’s scale of trillions of events per day
- You’ll join a team of experienced researchers and practitioners who have been conducting research in this space for over a decade, working closely with engineering and product counterparts to ensure research consistently translates into production and real business outcomes
- InMobi maintains deep investment in the compute and infrastructure this kind of work demands — including GPU compute, training, and inference platforms. This is a rare opportunity to shape how personalization and targeting operate at truly planetary scale
- Build a pipeline of evidence-backed hypotheses and evaluate them through to a clear accept/reject decision, taking the winners to production
- Drive architectural research into attention and post-attention architectures while meeting the millisecond-level latency demanded by real-time advertising
- Advance our evaluation methodology across offline and online settings, ensuring model improvements translate into real business impact
- Leverage the breadth of data features across our product portfolio to improve our models and help guide product teams toward sourcing high-value data
- Contribute regularly to our internal model implementations, training pipelines, and documentation to help the team operate efficiently and share knowledge effectively
- Guide our infrastructure teams on medium- and long-term needs to increase our experimentation velocity
- Maintain an active research profile through external engagement — publishing, giving talks, and participating in top conferences- Fluency in the PyTorch or JAX ecosystem, with the ability to implement complex operations in vectorized form
- Candidates nearing graduation are also welcome to apply
- PhD in machine learning, data science, or a related field preferred; Master’s degree also considered, particularly with a strong industry track record
- Expertise in deep recommender systems, including two-tower and attention-based architectures
- Understanding of the ads ecosystem (OpenRTB, SSPs, DSPs, ad exchanges, MMPs) is a plus
- Comfort with large-scale distributed data systems and the ability to build production data pipelines for training and inference
- Demonstrated expertise in machine learning/deep learning, whether through peer-reviewed publications (e.g., NeurIPS, KDD, RecSys) or through industry experience building and shipping models that performed well at scale
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