Used Tools & Technologies
Machine Learning GPURequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 6
Python @ 4
SQL @ 4
Spark @ 4
Statistics @ 4
ETL @ 4
Algorithms @ 4
Data Structures @ 4
MLOps @ 4
Data Science @ 6
Leadership @ 4
Mathematics @ 4
Networking @ 4
KubeFlow @ 4
MLFlow @ 4
Databricks @ 4
NLP @ 4
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
GeForce NOW (GFN) provides high-performance gaming to millions, regardless of their hardware. Within the Voice of the Customer (VOC) team, the role bridges petabytes of telemetry and the human experience. The team builds systems that explain why events happen, translating massive unstructured datasets into precise engineering actions that influence cloud streaming performance, security, and user retention. This role is focused on architecting production systems (not just reports) that detect anomalies in real time and correlate qualitative feedback with quantitative system performance.
Responsibilities
- Design and deploy production-ready algorithms that automate root-cause analysis for global streaming issues.
- Build pipelines to process and analyze forum discussions and direct feedback, using semantic chunking and vector databases to link sentiment to technical telemetry.
- Own systems across the full GFN software stack to identify data inconsistencies and refine models that predict churn and capacity needs.
- Build automated ETL workflows that transform raw logs into actionable signals for engineering and business leadership.
- Collaborate directly with product and infrastructure teams to turn statistical patterns into prioritized product roadmaps.
Requirements
- B.S., M.S., or PhD in Computer Science, Statistics, or Mathematics (or equivalent experience) with mastery of probability and statistical modeling.
- 5+ years of proven experience in data science or related roles.
- Expert-level Python: write modular, testable, production-ready code.
- Extensive experience with Spark, SQL, and Databricks for working with massive datasets where efficiency is required.
- Practical experience in NLP and text processing, including handling vector databases and other unstructured data structures.
- Proficiency in supervised and unsupervised learning, with strong focus on time-series analysis and anomaly detection.
Ways to Stand Out
- Experience maintaining production ML pipelines with MLOps tools such as MLflow or Kubeflow.
- Experience leveraging NVIDIA GPUs for large-scale data processing and model training.
- Domain expertise in cloud infrastructure, networking, or streaming technologies.
Compensation & Additional Info
- Base salary range: 152,000 USD - 241,500 USD (final base salary determined by location, experience, and pay of employees in similar positions).
- Eligible for equity and benefits.
- Applications accepted at least until July 17, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.