Jobs / United States / Adobe INC
Principal Scientist, ML – Overall Architect
Adobe INC · 🇺🇸 San Jose
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 1,036 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 221 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 Adobe INC paid sponsored hires in similar roles18 certified filings for “Applied Scientist” (Information Technology Project Managers) in CA: $174k–$215k, median $212k. Most were filed at wage level III (46%) — 3 lottery entries, ≈46% 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).
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Sponsor Radar — Adobe INC
The US Department of Labor certified 1,036 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 221 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 Adobe INC →
About the role
The Opportunity Adobe Applied Science & Machine Learning (ASML) is seeking a Principal Scientist, ML – Overall Architect to serve as the cross-cutting technical owner bridging our training and inference framework, ML model building, and data across Adobe's next-generation video and image foundation models. In this role, you will operate as the principal-level architect responsible for end-to-end coherence — holding the full technical picture across large-scale distributed training systems, inference and deployment, model architecture, and data recipe design, and making the architecture decisions that keep all of them aligned. Rather than owning a single system or model, your scope spans the seam between infrastructure, modeling, and data, ensuring these interdependencies are resolved at the architecture level rather than through coordination overhead. This role is intended for those who can operate with genuine authority at the intersection of systems, modeling, and data at scale — and whose technical judgment drives cross-team direction. Job Responsibilities End-to-End Architecture Ownership Own the overall technical architecture spanning the Training & Inference Framework, ML model building, and data — making principled design decisions that keep training systems, model architecture, and data pipelines coherent and mutually reinforcing. Training & Inference Systems Leadership Provide principal-level technical direction for large-scale distributed training (FSDP, Tensor Parallelism, Pipeline Parallelism, checkpointing, fault tolerance) and inference/serving infrastructure, ensuring systems are performant, reliable, and cost-efficient at scale. Model Architecture & Data Co-Design Drive the interaction between model architecture choices, training recipes, and data design — identifying co-design opportunities and resolving architectural tensions across modeling and data teams. Cross-Team Technical Direction Serve as the primary technical authority connecting TIF, model building, and data teams — unblocking architectural seams, aligning on interfaces, and ensuring workstreams remain coherent as models scale. Architecture Decisions for Next-Generation Models Own architecture-level decisions for GenRender6 / Gen6.5 and GenEdit1, spanning training execution, inference deployment, model design, and data pipeline integration. Performance and Scalability Drive architecture choices that optimize for training and inference efficiency, model quality, and cost — translating scaling and deployment requirements into concrete architecture decisions. What You'll Need to Succeed • Education: PhD in Computer Science, Electrical Engineering, AI/ML, or a related field, or equivalent depth demonstrated through research contributions or principal-level systems impact. • Principal-Level Technical Breadth: Demonstrated ability to reason authoritatively across distributed training systems, inference/deployment infrastructure, model architecture, and data pipelines — and to make architecture decisions that span all of them. • Deep Distributed Training & Inference Expertise: Hands-on expertise with large-scale distributed training (PyTorch FSDP, tensor and pipeline parallelism, checkpointing, fault tolerance) and inference/serving for large generative models. • Model Architecture & Data Understanding: Strong understanding of how model architecture decisions interact with training infrastructure and data recipes, with the ability to drive principled co-design across these areas. • Cross-Team Architecture Leadership: Proven track record of driving technical direction across multiple teams, resolving architectural conflicts, and delivering systems at the principal/staff level in a complex organization. • Senior-Level Judgment & Execution: Abil