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About This Role
Join NVIDIA IT’s Enterprise AI & Automation team to develop and expand enterprise-grade agentic AI systems at one of the world’s most advanced AI companies. NVIDIA’s Enterprise AI Platform drives production AI agents that securely link with enterprise systems to boost employee efficiency and accelerate business results across engineering, IT, supply chain, finance, HR, and sales. We need a Principal or Distinguished Engineer–level architect who defines systems through direct construction. This role calls for a deeply involved technical leader writing code daily in Python and/or Go. They quickly develop prototypes using modern code-generation tools like Cursor, Claude Code, and Claude Cowork. The candidate must grasp infrastructure aspects from Kubernetes to GPU inference stacks and translate new agent development patterns into scalable platform capabilities. You will build NVIDIA’s enterprise agent architecture by delivering functional systems, developing reference implementations, and elevating the technical standards across the organization. This is not a strategy-only or governance-only role. Architecture authority is earned through production systems, measurable impact, and technical depth. If you prosper in unclear environments, rapidly move from concepts to operational systems, and view the full agent development process—create, sandbox, launch, observe, control, and continuously enhance using data-driven cycles—this position lets you define enterprise-grade agentic AI at NVIDIA scale. You will invent systems that incorporate persistent memory, controlled runtime environments, strict assessment, and GPU-powered performance, ensuring agents are intelligent, trackable, protected, and production-ready from day one. What You Will Be Doing:* Develop and deliver production-quality agentic AI systems from start to finish using Python and/or Go, covering Kubernetes deployment, agent runtimes, memory systems, orchestration, tool integration, and evaluation pipelines.
- Define and advance NVIDIA’s Enterprise Agentic AI architecture through practical implementations, reference systems, and production deployments—not abstract diagrams.
- Build and implement multi-agent orchestration patterns (planner, executor, reviewer, tool agents) using frameworks such as LangChain, LangGraph, or similar orchestration systems, with strong regression coverage and observability.
- Run fast, high-quality POCs on emerging agent architectures; harden successful patterns into reusable platform services, APIs, SDKs, and developer templates.
- Architect and implement data flywheels that continuously improve agent quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
- Embed security, guardrails, sandbox isolation, auditability, and policy enforcement directly into agent runtimes in partnership with security and governance teams.
- Evaluate, integrate, and extend open-source and third-party agent platforms; drive disciplined build-vs-use decisions based on performance, scalability, control, and long-term platform ownership.
- Collaborate closely with engineering, infrastructure, product, and business collaborators to align architectural direction with enterprise priorities and accelerate adoption.
What We Need to See:* Bachelor’s degree in Computer Science or related field or equivalent experience; Master’s or PhD preferred.
- 15+ years of experience building and shipping large-scale distributed systems with significant hands-on coding in Python, Go, or similar systems languages.
- Proven skill in quickly transitioning from an idea to a functional prototype and then to a robust, scalable platform solution.
- Proven track record in constructing agentic AI systems, including RAG pipelines, long-lasting memory models, multi-agent management (e.g., LangChain, LangGraph), tool frameworks, and evaluation infrastructure.
- Expert-level depth in Kubernetes, containerized workloads, networking, APIs, and secure enterprise integration patterns.
- Experience crafting benchmarking, regression testing, telemetry, and observability systems that measure agent quality, latency, cost, reliability, and safety.
- Comprehensive knowledge of performance tuning in hybrid environments, including GPU-based inference systems.
- Excellent collaboration skills with the ability to influence cross-functional collaborators, build positive relationships, and clearly communicate complex architectural concepts to both technical and business audiences.
Ways to Stand Out from the Crowd:* Proven experience delivering reusable developer-acceleration components such as SDKs, APIs, templates, reference implementations, and CI/CD automation.
- Experience integrating enterprise vector databases and retrieval systems, and working with agentic search and orchestration platforms such as Glean, Microsoft Copilot Studio, Google Agentspace, or similar enterprise AI ecosystems.
- Experience embedding fine-grained policy enforcement, access controls, sandbox isolation, and audit trails directly into AI runtimes.
- GPU-acceleration approach with experience optimizing model inference, batching strategies, memory utilization, and efficiency on NVIDIA hardware.
- Evidence of meaningful open-source contributions, including core commits, maintainership, widely adopted libraries, or public technical artifacts demonstrating system-level depth.
NVIDIA is widely recognized as one of the technology world’s most sought-after employers. We have some of the most forward-thinking and creative people in the world working here. If you are driven by impact, passionate about enterprise AI, and encouraged to develop the blueprint of agentic AI at scale, we want to hear from you. Apply today! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until March 2, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse 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 $272K-$431K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($351K) sits 66% above the category median. Disclosed range: $272K to $431K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
NVIDIA AI Hiring
NVIDIA has 77 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, AI Agent Developer. Positions span Santa Clara, CA, US, Austin, TX, US, Seattle, WA, US. Compensation range: $172K - $431K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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