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About This Role
We are seeking a Developer Relations Manager to lead strategic engagement within the horizontal AI platform and tools ecosystem in North America. In this role, you'll partner with companies working on data platform engineering to expand the use of NVIDIA's AI and accelerated computing platforms. We're looking for someone with deep technical depth in production AI systems who can guide partner product direction, drive library integration, and support them building on NVIDIA technologies. If you're passionate about influencing the next wave of AI companies, we'd love to hear from you.
What You'll Be Doing:
- Develop and maintain deep technical expertise in data platform engineering, serve as the trusted technical advisor for ISVs and startups building in that space.
- Understand partner workloads and accelerate adoption by integrating the NVIDIA software stack including libraries, SDKs, NIMs, and blueprints, into partner products and data pipelines, delivering measurable performance and scalability improvements.
- Drive partner onboarding and co\-innovation through technical enablement assets such as reference architectures, sample code, benchmark, and workshop content that accelerate deployment of production\-ready solutions.
- Engage with partner technical leaders to guide best\-practice integrations, solve complex architectural challenges, and establish structured collaboration cadences that surface emerging workflows and inform NVIDIA product and platform strategy.
- Build and expand a strategic ecosystem of AI platform and tools partners, track ecosystem and technology trends, and identify opportunities to scale NVIDIA adoption and ecosystem growth.
- Collaborate cross\-functionally with solution architects, engineering, product management, and accounts teams to strengthen partner engagement and optimize adoption strategies.
- Advocate for ecosystem technical requirements by channeling actionable feedback from field deployments into NVIDIA product and engineering roadmaps.
What We Need to See:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
- 5\+ years of experience in the technology industry across software engineering, developer relations, technical partnerships, solutions architecture, or product management, including 3\+ years of hands\-on experience in AI.
- Deep domain knowledge across enterprise data platform, including data engineering and pipeline acceleration, post\-training data preparation and curation, data pipeline for RAG and semantic search.
- Proven ability to lead complex, multi\-stakeholder technical engagements, align cross\-functional priorities, and drive execution across internal teams and external partners. Experience architecting, integrating, and scaling joint solutions with strategic ISVs or ecosystem partners is preferred.
- Excellent communication and stakeholder management skills, with the ability to explain complex technical concepts to both engineering and executive audiences.
Ways to Stand Out from the crowd:
- Deep familiarity with NVIDIA’s accelerated AI stack, RAPIDS, NeMo Curator, NeMo Data Designer, NeMo Retriever, cuDF, cuML, cuGraph, cuVS and other frameworks and CUDA\-X libraries, with experience integrating them into production platforms.
- Experience partnering with early\-stage or high\-growth startups/ISVs in fast\-paced, ambiguous environments.
- A strong builder mindset, with a track record of creating technical solutions, enablement assets, or ecosystem integrations from the ground up.
With competitive salaries and a generous benefits package, we are widely considered to be one of the world's most desirable employers! We have some of the most forward\-thinking and hardworking people in the world working for us and, due to outstanding growth, our best\-in\-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD \- 241,500 USD for Level 3, and 184,000 USD \- 287,500 USD for Level 4\.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 2, 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 $152K-$287K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($219K) sits 23% above the category median. Disclosed range: $152K to $287K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 22 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager, MLOps Engineer. Positions span Austin, TX, US, Santa Clara, CA, US, CA, US. Compensation range: $224K - $379K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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