Jobs / United States / Braze INC
Staff Applied Scientist
Braze INC · 🇺🇸 Austin
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 16 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. USCIS also records 4 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 Braze INC paid sponsored hires in similar roles2 certified filings for “Forward-Deployed Data Scientist” (Data Scientists) in TX: $132k–$136k, median $134k. Most were filed at wage level I (100%) — 1 lottery entry, ≈15% 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 — Braze INC
The US Department of Labor certified 16 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. USCIS also records 4 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 Braze INC →
About the role
At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you. WHAT YOU'LL DO Braze is seeking a Staff Applied Scientist to join our Predictive and Generative AI (PGAI) team. The team's mission is to deliver a truly engaging and personalized customer experience through the creation of ML and AI enhanced marketing solutions. We own those solutions end to end, from the models and the flexible training pipelines that build them for each customer to the high-throughput APIs that serve predictions into our messaging systems. You will help set the scope of what is possible for customer engagement at scale, and from that space of possibilities you will lead solutions from prototype to product and build the ML platform that runs them. As the Staff Engineer on the team, you will: • Identify and drive the transformative initiatives that change what the team can deliver, whether that's replatforming how we train and serve models, redefining how data science ships to production, or retiring a generation of infrastructure • Build and ship at high velocity. Staff at Braze is a hands-on delivery role; you carry the most complex initiatives yourself from design through production. Current examples include distributed model training and serving, model lifecycle management, and the pipelines that keep hundreds of customer-specific models healthy across regions • Own the team's technical vision and quality bar. Set direction across the product portfolio and the ML platform, define best practices, and anticipate problems before they reach production • Drive initiatives that span teams. Our solutions ship into messaging, analytics, and data platform surfaces, and you carry the technical relationships with those teams • Raise the team's engineering quality through design review, code review, and production readiness for ML systems, and mentor other senior engineers and data scientists • Connect technical decisions to customer and business outcomes, and represent the team's technical perspective to product and engineering leadership WHO YOU ARE • 8+ years building ML systems in production, with hands-on depth across data science, ML engineering, and ML operations. You have designed and trained models yourself, built the pipelines and services that run them, and operated them under production load • A technical leader who has owned direction for a team, led multi-quarter initiatives across team boundaries, and grown senior engineers, all while keeping a high personal output • Deep experience prototyping, refining, and deploying predictive models (supervised and unsupervised learning, neural networks, recommenders) with frameworks such as PyTorch and Tensorflow • Strong distributed systems fundamentals, designing for scale, reliability, and cost on the billio