Interested in this AI/ML Engineer role at NVIDIA?
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We are looking for a resilient product leader who can align teams in a shared mission and turn complexity into execution while driving a company\-level NVIDIA initiative at the intersection of agentic AI, cybersecurity, and AI safety. This role is ideal for someone who can turn a fast paced research and threat landscape into a clear product strategy, credible technical roadmap, and partner\-ready platform plan. As autonomous agents reshape software, infrastructure, and security operations, this is a once\-in\-a\-generation opportunity to define how enterprises defend, evaluate, and safely deploy AI agents at scale. This leader will guide NVIDIA's roadmap for security in the age of autonomous agents, including open evaluation harnesses, defensive agent swarms, secure agent runtimes, enterprise digital twins, and platforms built on NVIDIA's AI and accelerated computing stack, working across research, engineering, security, policy, legal, and go\-to\-market teams, while partnering deeply with leading security companies, hyperscalers, AI labs, government partners, and critical infrastructure operators.
What you'll be doing:
- Define the Product Strategy Turn emerging AI security risks, customer needs, and research breakthroughs into a focused roadmap for agentic AI security platforms.
- Lead Cross\-Functional Execution Align research, engineering, security, data, evaluation, runtime, trust and safety, legal, policy, and go\-to\-market teams around a clear plan.
- Build Evaluation and Safety Platforms Drive open evaluation harnesses, model and agent safety workflows, disclosure processes, and trusted reporting systems.
- Advance Secure Agent Runtime Architecture Shape product direction for secure runtimes, enforcement layers, BlueField, OpenShell, NeMo Security, and related platform capabilities.
- Develop Defensive Agent Systems Lead product thinking for defender swarms, ThreatOps workflows, cybersecurity expert agents, and enterprise\-ready security automation.
- Partner Across the Ecosystem Work with strategic partners, including hyperscalers, security vendors, AI labs, and enterprise customers, to validate use cases and bring solutions to market.
- Drive Narrative and Market Readiness Partner with developer relations, product marketing, communications, government affairs, and legal teams to shape the external story and support adoption.
What we need to see:
- 20\+ overall years in product management, cybersecurity, AI platforms, enterprise infrastructure, or equivalent technical leadership roles.
- 10\+ years leading complex cross\-functional initiatives with executive visibility and influence across research, engineering, security, product, and go\-to\-market teams.
- Deep Cybersecurity Judgment: Expertise across vulnerability discovery, exploit chains, infrastructure\-as\-code, enterprise security operations, security automation, and incident response.
- Strong AI Product Instincts: Familiarity with model evaluation, agent harnesses, secure runtimes, AI safety, and the limits of legacy detection\-first security.
- Strategic Execution: Ability to translate technical ideas into an executable product roadmap, architecture direction, partner plan, and measurable outcomes.
- Credibility with exceptional researchers, engineers, security operators, and executive partners.
- Outstanding Communicator: Able to ask sharp technical questions and explain complex topics clearly to both technical and non\-technical audiences.
- Academic Foundation: BS or equivalent experience required; advanced degree or equivalent experience preferred.
Ways to stand out from the crowd:
- Track record building or leading products in cybersecurity, AI safety, agentic AI, cloud security, infrastructure security, or enterprise security operations.
- Experience with AI agents, model evaluation frameworks, cyber ranges, digital twins, simulation environments, or autonomous defense systems.
- Deep understanding of GPU\-accelerated computing, AI software platforms, secure infrastructure, or enterprise\-scale AI deployment.
- Strong ecosystem experience with security companies, hyperscalers, AI labs, government partners, or critical infrastructure operators.
- Entrepreneurial attitude that thrives on building new product categories in ambiguous, high\-urgency environments.
With highly competitive salaries and a comprehensive benefits package, 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, and, due to outstanding growth, our special engineering teams are growing fast. If you're a creative and autonomous professional with a genuine passion for technology, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 480,000 USD \- 690,000 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 17, 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 $480K-$690K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($585K) sits 167% above the category median. Disclosed range: $480K to $690K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 26 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, AI Product Manager. Positions span CA, US, Santa Clara, CA, US, Austin, TX, US. Compensation range: $195K - $690K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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