Interested in this AI/ML Engineer role at NVIDIA?
Apply Now →About This Role
We are seeking a highly technical and strategic Senior Developer Relations Manager to join our team, with a focus on engaging developers within a key named software account. In this pivotal role, you will work directly with developers, SREs, and security and platform practitioners to drive integration of NVIDIA libraries, frameworks and models within Splunk, AppDynamics, ThousandEyes, and AI Defense. The ideal candidate combines deep technical knowledge with a passion for developer advocacy. They explain how the technology addresses complex, real\-world challenges in observability, security, and AI.
What You'll Be Doing:
- Develop and maintain deep technical expertise in software products (Splunk, AppDynamics, ThousandEyes, AI Defense). Serve as the trusted technical advisor, problem solver, and champion for developers in observability, security operations, and AI platforms. Partner with cross\-functional teams to drive adoption of libraries within those technologies.
- Accelerate critical workloads by demonstrating and integrating the NVIDIA software stack (APIs, SDKs, data integrations, apps, and reference architectures) into partner products, platforms, and pipelines.
- Guide partners and developers through onboarding and integration by providing technical resources—such as sample code, guides, and demo pipelines—to accelerate adoption across NVIDIA’s platform and foster co\-innovation of next\-generation solutions.
- Map and monitor the developer ecosystem to identify growth opportunities, collaborating with engineering, product management, and marketing to inform roadmaps and optimize partner adoption strategies.
- Engage partner technical leaders to drive best\-practice integrations and resolve technical challenges, using regular syncs to track adoption, surface new workflows, and channel critical field insights back to NVIDIA product teams.
- Grow and activate developer communities around accelerated software — representing co\-engineered products at conferences, meetups, and hackathons and delivering technical talks and hands\-on workshops.
- Drive API, SDK, and integration adoption across Splunk and the broader software ecosystem, championing the developer experience end\-to\-end.
- Surface developer sentiment and ecosystem trends, translating them into actionable intelligence that informs product direction and platform strategy.
What We Need to See:
- Bachelor’s or Master’s degree or equivalent experience in Computer Science, Engineering, or a related field.
- A minimum of 12\+ years of overall professional experience in the technology industry in software engineering, developer relations, technical partnerships, solutions architecture, or product management, including 5\+ years of direct hands\-on experience in observability, data analytics, monitoring, or security software (e.g., Splunk, Datadog, Elastic, Grafana, or comparable enterprise platforms).
- Proven experience leading, partnering, and scaling developer programs at major technology companies, with ISVs, or within relevant verticals.
- Significant technical depth in observability, security, and/or AI software — including logging and metrics pipelines, distributed tracing, SIEM/SOAR, cloud\-native architectures, and/or AI security frameworks — and contributions to product integrations and software libraries.
- Experience leading technical collaborations with engineering and product teams — including architectural design, code reviews, technical mentorship, and delivery of technical talks or workshops.
- Proven ability to structure and implement complex technical engagements, negotiate requirements, prioritize issues, and collaborate with internal or external collaborators (across sales, legal, product, or marketing teams as needed).
- Skilled at distilling complex technical concepts for audiences from engineers to executives, with end\-to\-end experience in defining, integrating, building, and jointly marketing solutions with strategic ISV partners.
Ways to Stand Out from the Crowd:
- Hands\-on experience building or optimizing observability and security solutions (e.g., data ingestion pipelines, SPL queries and dashboards, detection content, distributed tracing instrumentation).
- Familiarity with the Splunk platform (SPL, data onboarding, app development) or other software products (AppDynamics, ThousandEyes, Cisco AI Defense).
- Experience with AI/ML security, LLM application security, or AI observability use cases.
- Successful history of building and scaling developer communities and delivering impactful technical enablement programs.
- Expertise in building frameworks to capture developer sentiment and market shifts, translating real\-time ecosystem dynamics into actionable intelligence for product strategy and rationalization.
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 224,000 USD \- 356,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 15, 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 $224K-$356K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($290K) sits 60% above the category median. Disclosed range: $224K to $356K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 26 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Research Scientist. Positions span Santa Clara, CA, US, New York, NY, US, Redmond, WA, US. Compensation range: $224K - $488K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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