Interested in this MLOps Engineer role at NVIDIA?
Apply Now →About This Role
We're hiring 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 MLOps 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 to 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 MLOps, 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 account 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 MLOps lifecycle, including inference serving platform, scalable deployment, eval, governance and observability pipeline.
- 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, including TensorRT\-LLM, NeMo, Dynamo, NIM, RAPIDS, run.ai, CUDA\-X libraries, and related frameworks, 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 median for MLOps Engineer roles in our dataset (median: $209K across 26 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At NVIDIA, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills in Demand for This Role
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. 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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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