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About This Role
NVIDIA is seeking a highly technical Engagement Lead to drive integration of Generative and Agentic AI software with our highest\-priority Enterprise ISV partners. In this role, you will lead the technical engagement with a select set of the world's most strategic enterprises as the trusted advisor their Software Platform Architects trust, and the leader who unites everyone working the account behind one technical vision. You'll stay hands\-on, designing and shipping the production agentic systems that run their most demanding workloads, while accelerating adoption of NVIDIA's stack across the initiatives that shape their business.
This role demands versatility at the intersection of platform strategy, strong AI/ML background with hands\-on execution, and communication calibre required with senior leaders. It's a high\-impact role where you will grow a rare breadth of skills and stay close to the frontier. Agentic AI moves fast, and staying at that frontier for your partners is a key part of the job. You will work closely with NVIDIA Product, Engineering, and Research, and your insights on real production deployments will directly shape what they build.
What You Will Be Doing:
- Lead the technical engagement with a focused set of ISV partners, working closely with their Software Platform Architects. Together you'll set the objectives, timelines, and adoption plan for each account.
- Stay hands\-on: design and ship the code, methods, and reference architectures that bring RAG, inference, and multi\-agent, long\-horizon workflows to life on our stack (NeMo, Nemotron, NeMo Agent Toolkit, NIM, Dynamo, TensorRT\-LLM) and open tools (vLLM, LangChain, vector DBs, MCP, A2A).
- Lead the technical strategy and execution: run the cadence, track adoption, and share what you learn with our Product, Engineering, Research, and Solution Architecture teams to land the best solution.
- Build breadth across the agentic AI lifecycle, with depth in a few areas that fit you: fine\-tuning (PEFT, SFT), post\-training and RL from verifiable rewards, reasoning, advanced RAG, multi\-agent workflows, skills/harness engineering, agent evaluation and observability, and production inference.
- Be the voice of the partner inside NVIDIA. Bring their needs and architecture to our Product, Engineering, and Research teams, and help shape the roadmap with what you see across industries.
- Grow deep expertise in agentic platform architecture, and keep up with our fast\-moving field and stack so you can give partners the best guidance.
What we need to see:
- 12\+ years in technical Product, Engineering, or Solutions roles across enterprise software and production AI, including customer\- or partner\-facing work.
- Masters or PhD in Computer Science, Electrical Engineering, or equivalent experience.
- A strong AI/ML and deep learning foundation, with hands\-on experience building enterprise\-grade GenAI systems: advanced RAG, multi\-agent architectures, and production LLM deployments.
- You enjoy leading technical engagements with customers, earning the trust of senior engineers, architects, and executives, and bringing a cross\-functional team along.
- Proficiency in Python and PyTorch and the modern agent/LLM stack (LangChain, an inference engine like vLLM or TensorRT\-LLM, vector DBs, MCP/A2A). Direct experience with every tool is not required.
- Solid grasp of enterprise deployment: MLOps/LLMOps, Kubernetes and Docker, and security, compliance, and governance.
- The range to research, prototype, and collaborate across teams, and execution rigor to carry the best solution through to production.
Ways to Stand Out from the crowd:
- Track record of influencing complex product decisions through trusted partner relationships, showing empathy for customer needs and an instinct for translating those into scalable platform improvements.
- Proactive in anticipating market trends, and advocating for innovation inside and outside the org, with an in\-depth knowledge of the ISV and cloud provider landscape.
- Demonstrated agility in high\-stakes environments required to deliver successful outcomes with partner collaborations especially with high\-velocity GenAI landscape.
- Strong growth and solution outlook, highly collaborative standout colleague, able to build deep trust with engineers, executives, and multi\-functional teams at both NVIDIA and Partner organizations.
Ready to shape how the biggest software companies build agentic AI? Come build it with us! You'll have the support of our Product, Engineering, Research, and Solutions teams, and work that reaches far beyond any single product. There has never been a better time to join!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD \- 356,500 USD for Level 5, and 272,000 USD \- 431,250 USD for Level 6\.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 18, 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 $224K-$431K 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 Required
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($327K) sits 50% above the category median. Disclosed range: $224K to $431K.
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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