NVIDIA is actively hiring for 26 AI and machine learning positions across AI/ML Engineer (22), AI Product Manager (2), and AI Software Engineer (1) roles. Posted salary ranges span $195K - $690K, with 100% of listings disclosing compensation. The median posted ceiling sits at $342K. Positions are based in CA, US, Santa Clara, CA, US, Austin, TX, US. The most frequently requested skills across these postings are Python, Kubernetes, Pytorch, Rag, Langchain. Senior-level roles account for 65% of openings.
Skills & Technologies
Locations
CA, US, Santa Clara, CA, US, Austin, TX, US, TX, US
Hiring by Role Category
Open Positions (showing 25 of 26)
Senior Developer Relations, AI - Security
Senior Director, Enterprise Software Engineering and AI Platforms
Senior Applied AI Engineer
Solutions Architect, Energy OT and Industrial AI
Solutions Architect, Physical AI and Omniverse
Senior Solutions Architect, Agentic AI
Senior AI Compiler Engineer, Algorithms and Code-Generation
Solutions Architect - Rack Scale AI Systems
Senior Platform AI Engineer
Engagement Tech Lead, Agentic AI
Applied Agentic AI Lead, Partner Co-Design
Senior Data Scientist, Voice of the Customer - GeForce NOW
Compiler Engineer, AI Inference- New College Grad 2026
Senior Solutions Architect, AI Infrastructure
Senior Product Manager, Physical AI Robotics Data
AI-Native Technical Program Manager
Solutions Architect, Agentic Optimization
Senior Product Manager, AI Physics
Developer Relations Manager, Local AI Ecosystem
Senior Technical Marketing Engineer, Enterprise AI Software
What NVIDIA's hiring tells you
With 26 active AI roles spanning 4 role types, hiring at this scale signals AI is core to the business model, not a pilot. Companies in this tier typically have a named AI leader (VP AI, Head of ML), dedicated infrastructure budget, and a multi-year roadmap. Posted compensation range ($195K - $690K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in AI/ML Engineer roles. That is a clue about what NVIDIA is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the NVIDIA interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- How is the AI org structured, and who does it report to (CTO, CEO, separate AI leader)?
- What was the most recent ML system that shipped to production, and what was the scope?
- How much of compute spend is on inference vs training, and how is that decided?
NVIDIA AI and ML Hiring
NVIDIA has 26 active AI and ML roles in our dataset. Open positions span AI/ML Engineer, AI Software Engineer, Data Scientist, AI Product Manager. Compensation ranges from $195K - $690K across disclosed roles. Roles are based in CA, US, Santa Clara, CA, US, Austin, TX, US, TX, US.
Salary Benchmarks
The market median for AI roles is $217,500. AI/ML Engineer roles pay a median of $218,750 across the market. AI Software Engineer roles pay a median of $219,250 across the market. Data Scientist roles pay a median of $192,890 across the market. Top-quartile AI compensation starts at $272,100.
Skills NVIDIA Looks For
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.
AI Role Categories
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $218,750 median across 3,817 positions with disclosed pay.
AI Software Engineer
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Market compensation for AI Software Engineer roles: $219,250 median across 424 positions with disclosed pay.
Data Scientist
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Market compensation for Data Scientist roles: $192,890 median across 463 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $216,175 median across 270 positions with disclosed pay.
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.
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.
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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