Jobs / United States / Applovin Corporation
Software Engineer, Machine Learning
Applovin Corporation · 🇺🇸 Palo Alto, CA
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 29 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. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- 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. 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 Applovin Corporation paid sponsored hires in similar roles3 certified filings for “Senior Machine Learning Engineer” (Data Scientists) in CA: $180k–$276k, median $230k. Most were filed at wage level IV (67%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
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.
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Sponsor Radar — Applovin Corporation
The US Department of Labor certified 29 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. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Applovin Corporation →
About the role
About AppLovin AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com . To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others. AppLovin is seeking a Software Engineer with strong machine learning expertise to advance user signal and recommendation technologies across our advertising platform, which reaches more than 1 Billion users globally. In this role, you will work on large-scale machine learning problems spanning user signals, representation learning, ranking, retrieval, model architecture, and optimization. In this role, you will work on large-scale machine learning problems spanning user signals, representation learning, ranking, retrieval, model architecture, and optimization. You will develop new ways to understand, represent, and utilize user signals and apply them to ranking and recommendation models. You will work across the ML stack from user signal and feature development to modeling, experimentation, and production to improve the relevance and performance of our advertising systems at scale. Responsibilities • Develop and improve user signals, features, and representations used by large-scale machine learning models for advertising and recommendation. • Explore machine learning approaches to learn effectively from large-scale, sparse, noisy, and heterogeneous user signals. • Improve the quality, coverage, and utilization of user signals, and measure their impact on downstream machine learning models and advertising performance. • Develop user representations and modeling approaches that effectively incorporate user signals into ranking, retrieval, prediction, and optimization systems. • Advance large-scale recommendation systems across candidate retrieval, ranking, prediction, and optimization. • Explore new model architectures and learning approaches to improve recommendation quality and advertising performance. • Develop scalable approaches for representation learning, feature interaction, and multi-task learning across large-scale user signals. • Identify and solve challenging ML problems spanning user signal quality, feature quality, model quality, training stability, data integrity, and serving performance. • Scale machine learning models and training systems to support increasing data volume, model complexity, and computational requirements. • Improve training and inference efficiency by identifying bottlenecks across model computation, data loading, memory utilization, distributed execution, and hardware utilization. • Build scalable tools and frameworks for user signal and feature evaluation, model training, experimentation, deployment, monitoring, and debugging. • Design and analyze offline and online experiments to understand the incremental value of user signals and model improvements and their impact on product and business outcomes. • Work closely with engineering, data, and product teams to bring new user signals and machine learning approaches from experimentation into production. Minimum Qualifications • Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience. • 4+ years of experience developing and deploying machine learning systems in production environments. • Experi