Interested in this AI/ML Engineer role at NVIDIA?
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NVIDIA is looking for a Solutions Architect to work in IPP's (Infrastructure, Planning and Process) Cloud Infrastructure Team. IPP is a global organization within NVIDIA. This group works with various other groups within NVIDIA such as Graphics Processors, Mobile Processors, Deep Learning, Artificial Intelligence and Driverless Cars to cater to their infrastructure needs. These cloud services provide almost half a million automated jobs per day on thousands of servers helping with the productivity of thousands of NVIDIA's software engineers worldwide. The cloud hosts a heterogeneous mix of machines and devices with various operating systems (Windows/Linux/Android), a multitude of hardware platforms both NVIDIA GPUs and Tegra Processors. Are you passionate about distributed infrastructure and looking for sophisticated, critical issues, ready to build the next generation of cloud services, design creative solutions, mine through data to uncover real problems and fix them?
What you'll be doing:
- Work with NVIDIA Product Teams to understand new product roadmaps and requirements primarily Rack Scale AI Products.
- Finding Optimum Solutions to deploy these products in a Datacenter or a Lab environment using sophisticated design techniques, services and tools.
- Assist in roll\-out and deployment of new development features aimed at supporting the latest NVIDIA hardware and technologies.
- Work closely with world\-class engineers, architects, technical product managers and application developers setting the best strategies in place for a product launch.
- Defining and implementing full scale solutions for product onboarding into our hosted and private cloud environments.
- Solve sophisticated problems involving multi\-site deployments of NVIDIA products and directly contribute to the overall quality of deployments and improve time to market next gen products.
- Collaborate with multi\-functional teams, including system engineering, software engineering, mechanical/thermal engineering, operations, data center teams, external vendors, and other partners to successfully deliver a reliable and robust platform from concept to prototype to deployments.
- Integrate and Optimize Cluster Deployment methods and manage SW stack deployments, including provisioning these services into the cloud.
What we need to see:
- Bachelor's or Master's Degree in Computer Science or Software Engineering, or equivalent experience.
- 12\+ years of relevant experience.
- 6\+ years of Linux and Scripting experience.
- Solid background on OS Kernels and system engineering.
- A track record of quickly understanding new technologies outside of your domain expertise and deploying systems in sophisticated configurations from hardware through multiple layers of software in a fast\-paced environment.
- Strong technical skills and understanding of embedded systems, orchestration \& automation systems, data centers and cloud architecture, as well as excellent communication and planning skills.
- Strong problem\-solving ability and experience in product engineering/failure analysis and debug/ HW or test design.
- Understanding of dense datacenter design including compute, Storage and networking.
Ways to stand out from the crowd:
- Understanding of software engineering principles and enterprise system architecture.
- Experience with gpu and compute clusters administration \& automation.
- Experience in large scale QA environments, for product bring ups.
- Special skills in large\-scale computing and cluster computing(MPI), data center design include high speed interconnect InfiniBand, Cluster Storage and Scheduling related design and/or management experience.
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High\-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens new universes to explore, enables outstanding creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. 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 passionate about new technologies we want you on our team!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 208,000 USD \- 333,500 USD for Level 5, and 256,000 USD \- 414,000 USD for Level 6\.
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 $208K-$414K 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($311K) sits 42% above the category median. Disclosed range: $208K to $414K.
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
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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