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About This Role
We are looking for a Principal Manager to join NVIDIA’s Automotive Product Management team with a focus on agentic workflows for autonomous vehicle (AV) development. In this role, you will define, design, and drive the implementation of AI\-agent\-powered workflows that accelerate how our engineering teams build, test, and deploy AV software. You will work at the intersection of generative AI, autonomous driving, and developer tooling to create reusable agents, skills, and orchestrated workflows that developers across NVIDIA and our partners can adopt in their own pipelines.
You will collaborate with forward\-thinking engineers, researchers, and business leaders across NVIDIA to translate the latest AI capabilities into productized agentic solutions—packaging them so they are discoverable, composable, and ready for integration into real\-world AV development environments.
What You Will Be Doing:
- Define the product vision, architecture, and roadmap for agentic workflows that support AV development—from data curation and labeling, to simulation, training, validation, and deployment
- Partner with engineering teams to design and implement end\-to\-end agentic workflows, specifying the agents, skills, tools, and orchestration logic required at each stage
- Own the packaging and productization of agents, skills, and workflows so that internal and external developers can discover, configure, and integrate them into their own AV development pipelines
- Synthesize requirements from AV engineering teams, customers, government regulations, and competitive benchmarking to prioritize the highest\-impact workflow capabilities
- Drive execution across teams (SW, HW, Safety, Operations, Integration) through technical engagements including architecture reviews, metric tracking, and sprint\-level planning
- Define use cases, acceptance criteria, and success metrics for each agentic workflow; ensure system design, safety, and quality are aligned with product goals
- Own stakeholder alignment and negotiate feature roadmaps and prioritization with internal teams and external customers
- Ensure development proceeds on schedule; proactively identify risks, resolve blockers, and maintain a program risk register
- Support customer engagements and pre\-sales discussions to demonstrate agentic workflow capabilities and align on deployment plans
- Develop and promote standards, tools, templates, and documentation that enable developer self\-service adoption of agentic workflows
What We Need to See:
- Strong understanding of ML/AI principles and technologies, with particular depth in large language models (LLMs), generative AI, and agentic AI frameworks (e.g., multi\-agent orchestration, tool use, retrieval\-augmented generation)
- BS/MS (or equivalent experience) in Engineering, Computer Science, or a related field and 15\+ years of product management experience spanning both software and hardware platformsDemonstrated experience defining and shipping AI\-powered developer tools, platforms, or workflow automation products
- Experience with autonomous vehicle development pipelines or ADAS—including data management, simulation, training, and deployment—is strongly preferred
- Familiarity with on\-device or edge computing as it relates to deploying AI models in automotive systems
- Self\-starter with a ‘can\-do’ attitude who thrives in a fast\-paced, multifaceted environment
- Experience working in agile software development environments with modern product requirements and project management tools
- Proven track record of leading the launch of technically complex products to customers
- Strong technical leadership and communication skills, with the ability to drive alignment across large organizations and with customers
Ways to Stand Out from the Crowd:
- Hands\-on experience with NVIDIA’s technology stack, including CUDA, TensorRT, Omniverse, Cosmos, Isaac, DRIVE, NIMs, or NeMo
- Experience building agentic AI systems from concept to production—including agent design, skill libraries, workflow orchestration, and developer\-facing APIs
- Demonstrated experience with OpenClaw, NemoClaw, Hermes, LangChain
- Experience deploying AI solutions in regulated or safety\-critical production environments
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 240,000 USD \- 379,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 24, 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 $240K-$379K 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 ($309K) sits 42% above the category median. Disclosed range: $240K to $379K.
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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