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About This Role
For over 25 years, NVIDIA has been revolutionizing computer graphics, PC gaming, and accelerated computing. 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 be,a ,,ast talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join the team and discover how you can build a lasting impact on the world.
NVIDIA's Silicon Co\-Design Group (SCG) is seeking Senior AI Platform Engineers. They will set the technical direction and lead the end\-to\-end delivery of the efficiency platform that supports our intelligent automation ecosystem.. If you are energized by building foundational platforms at the intersection of ML infrastructure and large\-scale systems, this is your opportunity!
What you'll be doing:
- Define the architectural direction, infrastructure investments, and roadmap priorities for the efficiency platform — across silicon architecture, build, methodology, validation, and applied AI teams.
- Define platform contracts and onboard new agents and skills from the domain teams across SCG building on the platform.
- Owning end\-to\-end delivery of the platform — from design and implementation through sustained production operation — with accountability for security, reliability, performance, and evolution.
- Leading the unified solutions including orchestration patterns, authentication and authorization, observability, and SLA enforcement. Driving platform\-wide decisions with multi\-functional impact. Managing storage and caching strategies that scale across heterogeneous compute environments.
- Serving as the technical authority for infrastructure powered by artificial intelligence across SCG: setting engineering standards, resolving cross\-team architectural challenges, and mentoring senior engineers.
What we need to see:
- BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, with 8\+ years of hands\-on experience designing and operating production\-grade platform or backend infrastructure.
- 5\+ years of direct ML infrastructure experience, including end\-to\-end ownership of a model serving platform or latency\-sensitive backend service from initial architecture through sustained production operation.
- Demonstrated track record of setting technical direction at the department or company level: defining platform strategy, establishing architectural standards, and leading initiatives spanning multiple teams.
- Strong Python skills and proficiency in at least one compiled language such as C, C\+\+, Go, Java, or Rust.
- Hands\-on experience with job queues \+ sandboxed execution (Kubernetes Jobs, Celery/Sidekiq/Temporal, container runtimes with resource isolation).
- Strong communication and leadership skills, with the ability to align senior team members and drive architectural decisions across organizations with contending priorities.
Ways to stand out from the crowd:
- Industry recognition in ML infrastructure or distributed systems — through publications, conference talks, open\-source contributions, or technical leadership visible beyond your current organization.
- Experience driving platform architecture at company scale, including engineering standards or frameworks broadly adopted by other teams.
- Exposure to silicon design, methodology, validation or EDA toolchains, especially the cadence of chip development lifecycles.
- Experience building or operating AI platforms within a silicon development, validation or EDA environment, with a firsthand understanding of the reliability and scale demands of chip design toolchains.
- Track record of mentoring senior engineers and growing technical talent — shaping the capabilities of the team as much as the platform itself.
The platform you architect will run every workflow involving NVIDIA silicon — from bring\-up, characterization, and debug to production sign\-off. If leading the foundation that the next generation of SCG engineers builds on is the problem you want to solve, we'd like to talk!
Known throughout the technology industry as a highly attractive employer, NVIDIA delivers highly competitive salaries and a comprehensive benefits package. As you consider your future, explore what we can provide for you and your family at www.nvidiabenefits.com/
\#LI\-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD \- 287,500 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 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 $184K-$356K 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 ($270K) sits 24% above the category median. Disclosed range: $184K to $356K.
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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