Our team is building an innovative Data Platform that employs advanced analytics, including prescriptive modeling and constrained optimization, for real-time routing and scheduling at scale.
This platform encompasses data collection, processing, visualization, analysis, anomaly detection, root cause identification, and predictive modeling. Our data include GPU availability/lifecycle, latency measurements from end users to data centers, game performance across different GPU types, and queuing information. Our active projects include applying optimization techniques to the cloud gaming experience; developing user behavior profiling, user base segmentation, actionable cluster detection, effective personalized recommendations, lifetime value analysis, and critical areas such as capacity management, prescriptive scheduling, and subscription churn analysis. We also focus on time-series forecasting, decision-making models, resource allocation, and latency minimization.
You will wield the power of Data, AI, and Operations Research to help deliver a best-in-class cloud streaming performance and experience to our users across the world. Our technology stack relies on industry-standard components (Python, SQL, Delta Lake, Apache Spark, Databricks, MLflow, Grafana, Elasticsearch).
What You will be Ding:
Build and deploy scalable ML/AI and optimization models to enhance demand forecasting, optimize capacity allocation, and develop user-specific feature engineering for real-time cloud gaming services.
Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes.
Acquire and apply domain knowledge of the product and software stack to identify and drive the resolution of data inconsistencies and improve model performance, especially in the context of optimization outcomes.
Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions.
Design and implement improvements to real-time prescriptive scheduling pipelines, using techniques like linear programming and constraint optimization, to enhance capacity utilization and user retention.
Improve productivity of the organization by mining petabytes of data for actionable insights for business and engineering, often through prescriptive recommendations.
Collaborate with a variety of partners to understand requirements, design robust solutions, and guide the team to deliver impactful results.
Leverage agentic AI to deliver best-in-class automation and programming solutions for complex analytical problems.
What We Need to See:
BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a strong emphasis on prescriptive analytics and optimization.
Strong background knowledge and practical experience in probability, statistics, AI/ML, prescriptive modeling, and optimization methodologies (e.g., linear programming, network flow, decision theory, and multi-armed bandit).
Strong coding skills, including the ability to write readable, testable, maintainable, and extensible code (primarily Python), with experience in libraries or tools relevant to optimization (e.g., Google OR-Tools).
Experience with common tools for data storage and processing, including drilling into problems of running large-scale software across large clusters
Strong experience in data cleaning, aggregation, transformation, and extraction, with an understanding of how data quality impacts performance.
Ways to Stand Out from the Crowd
Good interpersonal and presentation skills in working with multiple partners, adept at explaining intricate analytical solutions and their business implications.
Experience in time series analysis and forecasting for demand prediction in optimization contexts is a plus.
Experience in active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models is a plus.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
You will also be eligible for equity and benefits.
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.#deeplearningSkills Required
- BS/MS with 6+ years experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative field
- Proven experience in prescriptive analytics and optimization (linear programming, network flow, constrained optimization, decision theory, multi-armed bandit)
- Experience designing and deploying scalable ML/AI models for real-time systems and optimization outcomes
- Strong coding skills in Python with ability to write readable, testable, maintainable, extensible code
- Experience with optimization libraries/tools (e.g., Google OR-Tools)
- Experience with data storage and processing at scale (Delta Lake, Apache Spark, Databricks) and debugging cluster software issues
- Strong experience in data cleaning, aggregation, transformation, and extraction and understanding data quality impacts
- Experience in time series analysis and forecasting for demand prediction in optimization contexts
- Experience with ML production pipelines and monitoring (MLflow, Kubeflow)
- Strong interpersonal and presentation skills for cross-functional collaboration
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
