Jobs / United States / Moloco INC

Moloco INC

Staff Software Engineer - AI Applications

Menlo Park, California, United States; Seattle, Washington, United States Posted today

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Skills this job asks for

Machine learningRubyBudgetingLLMs

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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 53 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • 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.
  • What Moloco INC paid sponsored hires in similar roles13 certified filings for “Machine Learning Engineer” (Software Developers) in CA: $162k–$188k, median $162k.
  • 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 53 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 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 Moloco INC paid sponsored hires in similar roles
13 certified filings for “Machine Learning Engineer” (Software Developers) in CA: $162k–$188k, median $162k. Most were filed at wage level II (46%) — 2 lottery entries, ≈31% 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

About Moloco: Moloco is an AI-native performance advertising company, bringing the power of AI advertising to the open internet. Founded in 2013 by machine learning engineers who had built AI advertising systems inside the world's largest technology companies, Moloco began from a shared mission: to build that same engine for the open internet. Today, Moloco is one company with two businesses, built on the same compound AI system. Moloco Ads is our AI-native performance advertising platform, helping mobile app marketers acquire, retain, and re-engage high-value users across the open internet. Moloco Commerce Media is our AI-native retail media platform, enabling retailers and marketplaces to turn their platforms into thriving ad businesses. Both are powered by CARA (Compound Ad Recommendation Architecture) , the AI system behind every Moloco product. At Moloco, we bring together those who've already solved hard problems and those with the drive to. Here, we work on some of the complex problems in AI advertising, at a scale and level of sophistication very few companies can offer. Come build what's next. The Impact You'll Be Contributing to Moloco: Our mission is to change how Moloco operates as a company. AI makes it possible to rebuild how work gets done — not to shave minutes off tasks, but to redesign entire workflows so every Molocan operates with the leverage of a much larger team. Two principles will guide the team. • Outcomes first — AI will be a tool, not the goal; teams will pick the best solution for Moloco with the same engineering rigor as any other eng org. • Shared foundations, not central control — common infrastructure, guardrails, security, and standards that every team will build on autonomously. Three core pillars: • Enable safely. Safe rails for every Molocan — hosting for AI-developed apps, key management, spend caps, permissions and provisioning. With proper guardrails, neither recent incident would have shipped unreviewed or run unbounded. • Rebuild workflows GenAI-first. Redesign the company’s most critical workflows from the ground up around AI — the customer lifecycle from onboarding to campaign management and upsell, the recruiting pipeline from sourcing to offer, quarterly planning and forecasting. • Rationalize the stack. Evaluate, consolidate, and selectively replace SaaS where AI-first custom is better — e.g., the BI consolidation and open-weights proposals below. The Opportunity: This role is part of the AI Applications team, an extension of Moloco's generative AI initiative. This team sits higher in the AI stack, closer to the user, and focuses on the delivery layer rather than infrastructure. The primary mission is to build AI-powered products and tools that: • Make Moloco as a company operate more efficiently internally. • Improve the internal-to-customer experience using generative AI. • Improve the end customer experience with AI (lighter focus initially). • Much of the work is greenfield, with only small parts of the foundation currently in place. Engineers will own products end-to-end, from problem identification through launch and ongoing operations. How Do I Know if the Role is Right For Me? • 8+ years of professional software engineering experience, with a track record of owning complex, cross-functional systems end-to-end • Deep hands-on experience architecting LLM-powered applications at production scale — agentic systems, RAG, or tool-using assistants — including the tradeoffs between latency, cost, reliability, and quality • Experience with agent frameworks/orchestration (e.g., LangGraph, custom agent loops) and multi-step reasoning systems • Proven experience designing evaluation and monitoring systems for AI products that others on the team adopt and build on •

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