Jobs / United States / Nvidia Corporation
Machine Learning Engineer
Nvidia Corporation · 🇺🇸 2 Locations
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 2,374 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 394 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 Nvidia Corporation paid sponsored hires in similar roles701 certified filings for “Engineer Senior Systems Software” (Software Developers) in CA: $173k–$214k, median $190k. Most were filed at wage level IV (82%) — 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 — Nvidia Corporation
The US Department of Labor certified 2,374 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 394 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 Nvidia Corporation →
About the role
NVIDIA is looking for a talented Machine Learning Engineer to drive the development, evaluation, deployment and end-to-end lifecycle management of our AI-powered systems. This role bridges advanced AI application development with robust software engineering and continuous automation. You will extensively apply AI agents and build automated testing frameworks. You will also implement secure continuous integration and deployment pipelines with GitLab. These actions ensure code quality and system resilience. A core component of this role involves deploying and scaling models efficiently across distributed infrastructure. You will manage GPU orchestration, prompt-tune models, and build advanced AI workflows using platforms such as Kubernetes, Ray, or Slurm. What you'll be doing: • Architect, deploy, and scale open-source models using distributed orchestration frameworks. Examples include container orchestration platforms like Kubernetes, distributed computing frameworks such as Ray, or workload managers like Slurm. These frameworks support highly available and fault-tolerant AI workloads. • AI Systems & Data Pipelines: Design and build machine learning systems and data pipelines. Design experiments, prompt-tune, evaluate, and deploy production-grade models and AI agents, implementing flexible mechanisms to benchmark performance and swap models quickly to fit evolving use cases. • Error & Gap Analysis: Run comprehensive model benchmarks, perform deep error and gap analysis on model outputs, and build analytics dashboards to communicate system performance findings effectively to stakeholders. • Independent Execution: Take high ownership of features from ideation to production, managing architectural choices, coordinating updates across both accessible and restricted code repositories, and community interactions. What we need to see: • You have a Master’s or PhD in Computer Science, Electrical Engineering, or a related field - or equivalent experience. • Python & Systems Engineering: 3+ years of professional experience writing production-grade, asynchronous Python, with a strong focus on decoupled, clean system architecture and design patterns. • AI tools & ML Frameworks: Deep experience building with LangChain, Hugging Face libraries, vLLM, and SGLang. Experience with ML frameworks like TensorFlow, PyTorch and Scikit-learn • Data analysis: Proficient in data analysis using Python (pandas, NumPy, or similar), able to extract insights from model evaluation results and communicate findings clearly to both technical and non-technical collaborators. • Deployment & Orchestration: Hands-on experience with production-grade model deployment, performance monitoring and analysis; and scaling using Kubernetes, Ray, or Slurm to manage multi-node cluster configurations. • Hardware & Scaling Optimization: Strong understanding of GPU memory management, and infrastructure-level tuning for high-throughput, low-latency AI inference workflows. • GitLab CI/CD & Security Automation: Advanced knowledge of GitLab pipelines, specifically building automated test jobs and integrating vulnerability scanners directly into the MR workflow. • Testing Toolchains: Expert familiarity with Python testing frameworks (e.g., PyTest), mocking libraries, and automated test generation frameworks for AI workloads. • Advanced Version Control: High proficiency in advanced Git workflows, including rebase strategies, cryptographic commit signing, and managing complex public/private repository mirroring. Ways to stand out from the crowd: • Experience with alignment/fine-tuning of LLMs, including regular LLMs as well as VLMs (Vision-Language Models) or any-to-text • Passion for AI and a demonstrated commitment to advancing the field through innovative research, pr