Full-Time
Engineering Manager
AI DevOps
Posted on 11/23/2025
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
Compensation Overview
$224k - $356.5k/yr
Company Historically Provides H1B Sponsorship
Santa Clara, CA, USA
Hybrid
Hybrid role; on-site in Santa Clara, CA, with remote work option.
 Engineering Management (1) |
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- Bachelor's/Master's degree in Computer Science, Engineering, or equivalent experience
- 4+ years leading DevOps/SRE organizations with direct SDET leadership experience
- 8+ years hands-on experience in software development, test automation, or infrastructure engineering with AI/ML or GPU-intensive workloads
- Proficiency in Infrastructure as Code (IaC) platforms: Terraform, Ansible, or CloudFormation with exposure to multiple cloud environments (AWS, GCP, Azure, OCI)
- Strong technical leadership in test automation frameworks, CI/CD pipeline development, and quality engineering practices
- Familiarity with containerization and orchestration tools such as Docker and Kubernetes for leading AI/ML workloads and GPU resources
- Proven success building and scaling teams in fast-paced, high-growth environments
- Effective interpersonal skills to collaborate with remote teams and build agreement
- Proficiency in Python, Rust, or related programming languages along with the capability to engage in architecture conversations
- Demonstrated history of operational proficiency encompassing 24x7 on-call oversight, SRE methodologies, and robust high-availability infrastructures
- Supervise a team of DevOps engineers with expertise in AI inference infrastructure, test automation (SDET), and Infrastructure as Code (IaC)
- Architect and implement scalable test automation strategies for AI inference workloads, including performance benchmarking and automated quality gates
- Lead the maintenance of our GitHub First public CI infrastructure, focusing on single/multi-GPU testing, Kubernetes multi-node GPU testing, and CSP validation
- Drive Infrastructure as Code efforts by employing Terraform, Ansible, and Kubernetes to support scaling across multiple clouds and lead GPU clusters effectively.
- Attain operational proficiency encompassing 24x7 on-call rotations, SRE methodologies, automated monitoring, and self-repairing systems to guarantee uptime exceeding 99.9%
- Lead release coordination, cost optimization, and management of multi-cloud deployments
- Experience with CI/CD (specifically GitHub Actions), releasing Open-source AI software
- Proficient in Deep AI/ML infrastructure with expertise in NVIDIA technologies such as CUDA, TensorRT, Dynamo and Triton Inference Server, including coordinating GPU cluster operations and GPU workload performance benchmarking
- Background in DevOps, system software testing, and previous experience leading teams on inference engines, model serving platforms, or AI acceleration frameworks
- Track record with monitoring tools (Prometheus, Grafana), security scanning, static/dynamic analysis tools, and license compliance automation for critical AI inferencing frameworks.
NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.
Company Size
10,001+
Company Stage
IPO
Headquarters
Santa Clara, California
Founded
1993
Simplify's Take
What believers are saying
- Data centers generate 89% of $215.9B FY2026 revenue.
- $40B acquisitions like OpenAI bolster AI infrastructure dominance.
- Nemotron models and Drive Thor accelerate agentic AI adoption.
What critics are saying
- AMD MI450X outperforms Blackwell by 25% per watt in inference.
- Huawei Ascend 910D blocks $10B China AI sales due to bans.
- Google TPU v6 cuts hyperscaler GPU dependency by 60%.
What makes NVIDIA unique
- NVIDIA invented GPU in 1999, pioneering accelerated computing.
- CUDA platform from 2006 enables GPUs for AI and HPC.
- Holds 92% discrete GPU market share as of Q1 2025.
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Growth & Insights and Company News
6 month growth
↓ -1%1 year growth
↓ -3%2 year growth
↓ -2%Matlantis has integrated NVIDIA's ALCHEMI Toolkit into its materials simulation platform to accelerate industrial materials discovery. The company previously incorporated NVIDIA Warp-optimised kernels, achieving up to 10x speed improvements in atomistic calculations. The integration includes LightPFP, Matlantis' lightweight potential for large-scale simulations, which uses a server-based architecture with NVIDIA ALCHEMI Toolkit-Ops to reduce communication bottlenecks. Matlantis plans to integrate its flagship Universal Machine-Learning Interatomic Potential with the toolkit to further enhance GPU efficiency. Launched in 2021, Matlantis is a cloud-based atomistic simulator jointly developed by PFN and ENEOS. The platform uses deep learning to increase simulation speeds by tens of thousands of times and serves over 150 companies discovering materials including catalysts, batteries and semiconductors.
Nvidia shares have climbed 18% over a ten-day winning streak, the longest since 2023. The stock is trading about 8% below its October all-time high of $212.19. CEO Jensen Huang revealed at last month's GTC conference that Nvidia has over $1 trillion in GPU orders through 2027, including Blackwell and next-generation Vera Rubin chips. Data centre revenue surged 75% year-over-year and now comprises 88% of the business, a dramatic shift from five years ago when gaming dominated. The rally follows major deals including Meta's February commitment to deploy millions of Nvidia chips across its global data centres. On Monday, Nvidia denied rumours it was pursuing acquisitions of PC makers Dell or HP. The company also unveiled Ising, a new family of open-source models for quantum computing.
D-Wave Quantum CEO Alan Baratz claims quantum computing poses a threat to Nvidia, citing superior energy efficiency. Speaking at the Semafor World Economy Summit, Baratz said D-Wave's quantum computer uses just 10 kilowatts of power—equivalent to five or 10 GPUs—whilst solving problems that would take GPU systems nearly a million years. D-Wave shares rose nearly 16% on Tuesday, part of a 140% gain over the past year. The company reported $2.75 million in Q4 revenue, missing analyst estimates, but bookings surged 471% to $13.4 million. The $5.3 billion company recently secured a $20 million agreement with Florida Atlantic University and acquired Quantum Circuits for $550 million. However, quantum machines remain specialised tools, unable to run large language models that drive Nvidia's dominance.
Vertiv Holdings has been reaffirmed with a Buy rating by Evercore ISI, setting a price target of $280, whilst Barclays raised its target from $281 to $300 with an Overweight rating. The electrical equipment company is partnering with Nvidia on AI infrastructure development. On 16th March, Nvidia introduced its Vera Rubin DSX AI Factory reference design, with Vertiv providing critical power and cooling solutions for AI data centres. The partnership integrates Vertiv's infrastructure expertise with Nvidia's AI systems to enhance energy efficiency and performance. Vertiv is developing Vertiv OneCore Rubin DSX, a prefabricated system designed to accelerate AI factory deployment. The Brussels-headquartered company specialises in critical digital infrastructure technologies for data centres and communication networks.
Nvidia and Dell Technologies are positioned as attractive AI infrastructure investments ahead of their May earnings reports, according to recent analysis. Both companies supply critical hardware for AI computing, with demand for AI capacity continuing to outpace available resources across major cloud services. Nvidia shares have remained flat for six months despite strong fundamentals. Last quarter, its data centre business generated $62 billion in revenue, up 75% year over year, with a 75% gross margin. The company expects over $1 trillion in cumulative orders for its Blackwell and upcoming Rubin chips through 2027. Trading at 17 times next year's expected earnings, Nvidia's valuation appears discounted relative to its 66% revenue growth in fiscal year 2026. Dell Technologies similarly stands to benefit from the AI infrastructure build-out. Both companies report earnings in May.