Interested in this AI/ML Engineer role at NVIDIA?
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NVIDIA has been redefining computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and outstanding people. Today, we’re tapping into the potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent!
NVIDIA’s Physical AI platform encompasses a suite of libraries, frameworks, and tools—including NVIDIA Omniverse™ (built on OpenUSD), Isaac for robotics, Metropolis for intelligent video analytics, and NVIDIA Cosmos™ for world foundation models—that enable enterprises to build, simulate, and deploy AI systems that perceive and interact with the physical world. The Physical AI segment sales team is looking for a high-impact, well-rounded Segment Sales leader who thinks strategically and is hands-on with sales play curation, enablement, and implementation. Experience working in global, cross-functional, matrixed, and collaborative settings will be valuable for this diverse, high-impact role. The position demands detailed and resourceful collaboration throughout the NVIDIA platform.
What you’ll be doing:
- Work with Enterprise customers and internal sales and technical teams to identify and prioritize end-to-end Virtual Facility Integration (VFI) solutions—leveraging Physical AI and digital twin technologies for factory and warehouse design, layout planning, operational simulation, and continuous optimization across industrial manufacturing environments.
- Establish and develop NVIDIA relationships with key CXOs and decision makers across the Enterprise, enabling customer references for future industry growth.
- Collaborate with NVIDIA sales leaders to develop Physical AI solutions for enterprise customers spanning factory planning, synthetic data generation, robotic simulation, and intelligent video analytics opportunities.
- Collaborate with product management, marketing, education services, training, sales operations, finance, and global sales teams to enable GTM by developing, communicating, and providing training for Physical AI Sales Plays.
- Maintain an opportunity tracker to communicate insights, blockers, and feedback on the market adoption of solutions and related growth strategy.
- Engage deeply with Account teams to provide strategy for Lighthouse Accounts.
- Represent the voice of enterprise customers and partners with insights to advise future product development and conceptualization.
What we need to see:
- 15+ overall years of work experience, including 5+ years driving enterprise product- and software business development work with major technology companies and innovative start-ups, preferably in industrial domain with cloud focus.
- 7+ years of management experience.
- Bachelor’s degree (or equivalent experience) in Engineering, Science, with manufacturing-related 3D digital twin technology.
- Proven success in building and managing end-to-end partner solutions including business case, solution definition, value proposition, sales enablement, and joint marketing.
- Excellent consultative and interpersonal skills that have resulted in C-level and functional VP business relationships of outstanding trust, confidence, and results.
- Shown ability to collaborate and influence cross-functional teams without formal authority with external partners and internal cross-functional teams at all levels.
- Demonstrable experience in technologies related to 3D systems, digital twins, physical AI, robotics, advanced hardware, industrial manufacturing domains, and next-generation computing technologies.
- Experience developing and implementing joint go-to-market strategies with ISV partners, including co-selling motions, solution packaging, and partner ecosystem enablement.
Ways to stand out from the crowd:
- Experience in immersive 3D, CAD, digital twins, Siemens/Dassault/Rockwell Automation portfolio, SDR, XR, Industry 4.0.
- Background in industrial automation or supply chain management.
- Previous experience as a hands-on Solutions Architect, Business Development, or Field support in industrial manufacturing and factory planning domain.
- Prior experience planning, crafting, and operating factory operations and affinity to customer difficulties in this domain.
- Experience working with developer communities and network partners to identify and deploy industry-specific use cases to drive incremental usage of NVIDIA technology.
NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Your cash compensation will be determined based on your location, experience and the pay of employees in similar positions with 85% paid through base salary and 15% variable compensation. The cash compensation range is 296,000 USD - 448,500 USD. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until September 20, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Salary Context
This $296K-$448K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NVIDIA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($372K) sits 76% above the category median. Disclosed range: $296K to $448K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NVIDIA AI Hiring
NVIDIA has 52 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US, Santa Clara, CA. Compensation range: $147K - $448K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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