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About This Role
Site Reliability Engineering (SRE) at NVIDIA is an engineering field focused on designing, building, and operating large-scale production systems with exceptional reliability, efficiency, and availability. It combines software and systems engineering practices with expertise across distributed systems, networking, Kubernetes, public cloud, observability, capacity management, continuous delivery, and automation.
As an Engineering Manager, you will lead a team of dedicated engineers responsible for building and operating resilient AI platform capabilities at enterprise scale. You will combine people leadership with strong technical judgment, helping the team translate ambiguous business and engineering challenges into a clear strategy and executable roadmap. You will partner across Cloud, Platform, Security, and AI/ML organizations to deliver reliable systems, improve developer productivity, and advance the use of AI agents and skills in platform operations.
Our culture values diversity, intellectual curiosity, collaboration, and continuous learning. We encourage thoughtful risk-taking, blameless analysis, and shared ownership. You will create an environment in which engineers can do their best work, grow their careers, and make a meaningful impact.
What you’ll be doing
- Lead, develop, and grow a team of SRE, platform, and software engineers responsible for NVIDIA’s AI Platform Runtime and related production services.
- Define the team’s technical strategy, priorities, and roadmap in alignment with broader product, platform, and business objectives.
- Guide the design and delivery of highly available, scalable, secure, and resilient distributed systems that support enterprise AI agent products.
- Drive the development of AI agents, AI skills, and intelligent automation for platform operations, incident response, troubleshooting, and remediation.
- Establish measurable reliability goals and effective operational practices using service-level indicators, service-level objectives, error budgets, capacity models, operational health metrics, and production readiness reviews.
- Improve engineering velocity and developer experience through self-service platforms, infrastructure-as-code, standardized delivery patterns, and automation.
- Partner with product managers, architects, and leaders across Cloud, Security, Networking, Platform, and AI/ML teams to coordinate initiatives involving multiple functions.
- Maintain a healthy balance among feature delivery, platform investment, operational work, reliability improvements, and technical debt reduction.
- Lead the team through critical incidents and ensure that blameless postmortems result in clear ownership and durable corrective actions.
- Recruit exceptional engineers and foster an inclusive, high-performing environment through coaching, feedback, career development, and thoughtful delegation.
What we need to see
- 10+ years of experience in Site Reliability Engineering, Platform Engineering, Software Engineering, Cloud Infrastructure, or a related technical field, including 3+ years managing or formally leading engineering teams responsible for complex production systems.
- Technical foundation in distributed systems, Linux, networking, Kubernetes, and public cloud platforms such as AWS, Azure, or GCP.
- Experience leading teams that build production software and automation using languages such as Python, Go, TypeScript, JavaScript, or Java.
- Solid understanding of observability at scale, including OpenTelemetry, metrics, logs, distributed tracing, profiling, and operational analytics.
- Experience applying SRE practices such as service-level objectives, error budgets, capacity and resource management, incident management, disaster recovery, and blameless postmortems.
- Demonstrated ability to create technical roadmaps, manage competing priorities, and deliver measurable outcomes across multiple teams.
- Proven ability to hire, coach, retain, and develop engineers at different career stages.
- Outstanding communication and collaboration skills, with the ability to influence across technical, product, and organizational boundaries.
Ways to stand out from the crowd
- Hands-on experience with AI agents, agentic workflows, or intelligent automation for infrastructure and production operations.
- A track record of improving availability, performance, operational efficiency, developer productivity, or incident response through measurable engineering initiatives.
- Experience building or scaling an SRE or platform engineering function across a large enterprise and coordinating complex programs across organizational boundaries.
- Success developing senior engineers and technical leaders, paired with a strong sense of ownership, curiosity, and empathy that enables you to build trust and bring clarity to ambiguity.
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 innovations that were once science fiction, from artificial intelligence to autonomous vehicles.
NVIDIA is looking for exceptional engineering leaders like you to build the teams and platforms that will accelerate the next wave of artificial intelligence.
Widely considered to be one of the technology world’s most desirable employers,
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. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 5, 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-$333K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($270K) sits 28% above the category median. Disclosed range: $208K to $333K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NVIDIA AI Hiring
NVIDIA has 52 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US, Santa Clara, CA. Compensation range: $147K - $448K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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