Applied Scientist - Real-Time Personalization at Scale

InMobi Advertising; San Mateo (CA); On-site; USD 182,000 - 219,000; Full time

Benefits offered by this job

RSU equity

Excellent medical/dental/vision

401(k) matching

Generous vacation and holidays

Flexible work hours

Paid parental leave

Free lunch at offices

Pet-friendly office

EAP

Job summary

InMobi Advertising is seeking an Applied Scientist to advance user personalization intelligence at planetary scale. You will develop architectures for attention-based models handling trillions of events daily, collaborating with engineers to translate research into production and measurable impact.

The role requires PhD/strong industry track record, deep ML/DL experience, and proficiency in PyTorch or JAX, plus recommender systems expertise and distributed data pipelines.

Qualifications

• PhD in machine learning, data science, or a related field preferred; Master's degree also considered, particularly with a strong industry track record.
• Demonstrated expertise in machine learning/deep learning, incl. at-scale models.
• Fluency in PyTorch or JAX, with vectorized implementation ability.
• Expertise in deep recommender systems, including two-tower and attention-based architectures.
• Comfort with large-scale distributed data systems and building production data pipelines.
• Understanding of the ads ecosystem (OpenRTB, SSPs, DSPs, ad exchanges, MMPs) is a plus.

Responsibilities

• Build a pipeline of evidence-backed hypotheses and move winners to production.
• Drive architectural research into attention-based models meeting millisecond latency.
• Advance evaluation methodologies offline and online to show business impact.
• Leverage diverse data features to improve models and guide data sourcing.
• Contribute to internal model implementations, training pipelines, and docs.
• Guide infrastructure teams on needs to boost experimentation velocity.
• Maintain an active research profile through talks and publications.

Job description

InMobi Advertising is seeking an Applied Scientist to advance user personalization intelligence at planetary scale. You will develop architectures for attention-based models handling trillions of events daily, collaborating with engineers to translate research into production and measurable impact.

The role requires PhD/strong industry track record, deep ML/DL experience, and proficiency in PyTorch or JAX, plus recommender systems expertise and distributed data pipelines.