Skills this job asks for
Machine learningDeep learningBudgeting
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Sponsorship verdict
Sponsorship possible
One solid signal, not two. Worth applying, and worth asking about sponsorship early.
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✓
Employer is on a government sponsor recordThe US Department of Labor certified 3 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest May 2026) — the step every H-1B hire needs first.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
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✓
What Inmobi INC paid sponsored hires in similar roles1 certified filing for “Data Scientist III” (Data Scientists) in CA: $220k–$220k, median $220k.
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Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Employer is on a government sponsor record
- The US Department of Labor certified 3 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest May 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
- No salary bar for this route
- H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Inmobi INC paid sponsored hires in similar roles
- 1 certified filing for “Data Scientist III” (Data Scientists) in CA: $220k–$220k, median $220k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.
About the role
InMobi (Corporate)
InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd.
InMobi Advertising
InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com.
Glance
Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com.
Overview of the Role:
We are looking for an Applied Scientist III to join our algorithmic and research science team. You’ll work on mathematically rigorous, research-driven problems at production scale. This role sits at the intersection of theory and application, designing algorithms that combine elegant modeling with measurable business impact. Specifically, our scientists tackle challenges across traffic shaping, fraud detection, ad quality, pricing strategies, and auction theory, along with their practical applications. We leverage the latest deep learning models alongside classical machine learning techniques to build innovative solutions.
As the heart of the InMobi Exchange, our team optimizes the company’s core business functions and creates the strategic moat that sets us apart in the market. You will not just “use models”—you will formulate them, evaluate their assumptions, tailor them to our problem domain, and bring them to life in production. Many of our challenges have no off-the-shelf solutions; we require scientific creativity to bridge research and reality.
If you thrive on solving complex, high-impact problems and want to see your ideas shape the future of a global exchange, this is the place where your work will truly make a difference.
Location : This role is based on-site in our San Mateo, CA office
The Impact You'll Make:
In this role, you’ll operate at the intersection of cutting-edge research and massive-scale production, shaping algorithms that power a global advertising marketplace, making tens of trillions of real-time decisions every day. You’ll work in an environment where models are continuously tested, evaluated, and refined — with rapid learning loops measured in hours, not weeks. Collaborating with a team that values algorithmic depth and scientific rigor, you’ll have the opportunity to prototype, publish, and deploy work that drives measurable business impact.
• Formulate, analyze, and implement algorithms that power real-time auctions, dynamic pricing, bid shaping, pacing, and traffic allocation across a massive-scale ad marketplace.
• Design and experiment with methods in online learning, reinforcement learning, multi-armed bandits, forecasti
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