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NVIDIA has been transforming 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 amazing people. Today, we’re tapping into the unlimited 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. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are seeking a technical and strategic Senior Business Development Manager to join our team, with a focus on growing business ecosystems across emerging technology domains, specifically Physical AI. In this pivotal role, you will work directly with software solution providers, strategic partners, and industry professionals to champion the adoption of NVIDIA's advanced AI and computing platforms. The ideal candidate brings a blend of deep technical expertise and commercial go\-to\-market experience, combined with a passion for business growth and a talent for communicating how NVIDIA technology can solve complex, real\-world challenges.
You must be a U.S. Citizen to apply for this position
What You'll Be Doing:
- Serve as the trusted strategic advisor, problem solver, and champion for the partner ecosystem in the Federal Physical AI Ecosystem, with cross\-functional partners to drive adoption of NVIDIA technologies.
- Accelerate critical business opportunities by demonstrating groundbreaking solutions that integrate the core NVIDIA stack into partner products, platforms, and pipelines.
- Advise go\-to\-market resources, such as solution briefs, business cases, demonstration pipelines, and tools to highlight the application of technologies in solving real\-world problems across the Federal and commercial ecosystem.
- Guide partners and startups through onboarding and integration with NVIDIA's programs, fostering co\-innovation and the development of next\-generation solutions.
- Map, track, and monitor the partner ecosystem to identify growth opportunities, inform technology roadmaps, and shape market expansion strategies.
- Collaborate multi\-functionally with solution architects, engineering, product management, and marketing to drive partner engagement and optimize adoption strategies.
- Engage with partner business teams, technical leaders, and decision\-makers to identify goals, solve business challenges, and promote best practices for successful integrations.
- Represent and advocate for partner needs and feedback to NVIDIA's internal product and engineering teams, supplying actionable insights from field engagements to influence product roadmaps.
What We Need to See:
- A minimum of 8\+ years of overall professional experience in the technology industry in business development, strategic partnerships, or technical sales, including 5\+ years of direct hands\-on experience in the Federal ecosystem.
- Bachelors degree or equivalent experience.
- Significant domain proficiency with the Federal Physical AI Ecosystem in high\-performance computing, cloud, AI/ML, and/or vertical\-specific frameworks and solutions.
- Excellent interpersonal skills with the ability to distill complex technical concepts for diverse technical and non\-technical audiences from engineers to executives.
- Experience leading strategic collaborations with engineering and product teams, including solution design, business case development, executive presentations, and delivery of technical or business workshops.
- Proven ability to structure and execute complex strategic engagements, negotiate agreements, prioritize opportunities, and collaborate with internal or external stakeholders (across sales, legal, product, or marketing teams as needed).
Ways to Stand Out from the Crowd:
- Hands\-on experience building or scaling vertical\-specific go\-to\-market solutions.
- Familiarity with advanced computing, AI, and/or GPU acceleration platforms.
- Track record in identifying and closing strategic partnerships for real\-time processing and low\-latency decision\-making use cases.
- Successful history of building and scaling partner ecosystems and delivering impactful business enablement programs.
- Established relationships within the Physical AI ecosystem.
With competitive salaries and a generous benefits package, we are widely considered to be one of the world's most desirable employers! We have some of the most forward\-thinking and hardworking people in the world working for us and, due to outstanding growth, our best\-in\-class teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, 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 200,000 USD \- 304,750 USD for Level 4, and 224,000 USD \- 356,500 USD for Level 5\.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 19, 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 $200K-$356K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($278K) sits 50% above the category median. Disclosed range: $200K to $356K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 21 open AI roles right now. They're hiring across Research Scientist, AI Software Engineer, AI/ML Engineer, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US, Washington, DC, US. Compensation range: $224K - $488K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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