Interested in this AI/ML Engineer role at NVIDIA?
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About This Role
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. 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 best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are looking for a Senior Solutions Architect to support our Industrial Engineering accounts — the CAE, CFD, and FEA software vendors, engineering\-simulation platforms, and industrial OEMs building the next generation of physics\-based and AI\-augmented engineering workflows on NVIDIA platforms. In this role you will be a trusted technical advisor to simulation and engineering software developers, embedding NVIDIA accelerated computing, Omniverse, and physics\-ML into solver, simulation, and digital\-twin pipelines. You will play a direct role in improving application performance, accelerating design and simulation cycles, and establishing the technical foundation required for next\-generation engineering and digital\-twin systems.
What you’ll be doing:
- Support Business Development and Sales teams as part of a small Solutions Architecture team, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive ecosystem success across Industrial Engineering accounts (CAE/CFD/FEA ISVs, simulation platforms, and industrial OEMs).
- Work directly with engineering\-software developers and customer simulation teams in a customer\-facing setting.
- Help developers GPU\-accelerate and scale CAE/CFD/FEA solvers and structural, thermal, and fluid\-dynamics workloads on NVIDIA accelerated computing and HPC platforms.
- Apply physics\-informed ML and surrogate modeling (e.g., NVIDIA PhysicsNeMo / Modulus) and NVIDIA Omniverse digital twins to compress design, simulation, and optimization cycles.
- Analyze simulation and engineering application architectures and find opportunities for acceleration.
- Provide feedback and collaborate with engineering, product, and research teams.
- Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.
What we need to see:
- BS/MS/PhD in Mechanical, Aerospace, Civil, or Chemical Engineering, Computational Science, Applied Mathematics, Physics, or a related technical field (or equivalent experience).
- 8\+ years working in CAE/CFD/FEA or computational engineering — numerical simulation, solver development, or HPC\-based engineering analysis.
- Hands\-on experience with commercial or open\-source simulation tools (e.g., Ansys, Siemens Simcenter, Altair, COMSOL, Cadence Fidelity CFD, OpenFOAM, LS\-DYNA, Abaqus).
- Strong grounding in numerical methods (FEM/FVM/spectral), linear algebra, and the mathematics behind physics solvers.
- Experience in algorithm programming using languages like Python and C/C\+\+, with familiarity GPU\-accelerating compute\-intensive workloads.
- Familiarity with accelerated computing platforms, GPU\-based distributed systems, and HPC clusters/schedulers (e.g., Slurm).
- Familiarity with containers, numerical libraries, modular software design, version control, GitHub.
- Experience designing, prototyping, and building complex solutions for customers; able to reason across components such as data pipelines, solvers, compute, networking, and orchestration.
- Solid written and oral communication skills and familiarity with collaborative environments.
- Team player who can learn, react, and adapt quickly, with an attitude to work in a fast\-paced environment.
Ways to stand out from the crowd:
- Experience GPU\-accelerating CFD/FEA solvers, or developing physics\-ML and surrogate models (NVIDIA PhysicsNeMo/Modulus, physics\-informed neural networks).
- Background with NVIDIA Omniverse, OpenUSD, and digital\-twin workflows for industrial and engineering simulation.
- Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA\-X math libraries (cuBLAS, cuSPARSE, cuDNN).
- Experience with Kubernetes, distributed training, and large\-scale inference.
- Experience supporting or using PCIe accelerators such as GPUs, FPGAs, DSPs from evaluation to production stages.
NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward\-thinking and hardworking individuals in the world working for us. If you're creative and autonomous, we want to hear from you!
\#NALASAHiring
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.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 14, 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-$287K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 10% above the category median. Disclosed range: $184K to $287K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NVIDIA AI Hiring
NVIDIA has 28 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Research Scientist. Positions span Santa Clara, CA, US, Austin, TX, US, CA, US. Compensation range: $195K - $431K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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