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What are the typical challenges faced in a Data Crowd Compute position?

Professionals in Data Crowd Compute roles often handle vast volumes of data that require creative and efficient processing solutions, which can present scalability and performance challenges. Coordinating compute tasks across distributed environments demands attention to system reliability, cost optimization, and data integrity. Team members frequently collaborate with data scientists, engineers, and stakeholders to prioritize workloads and deliver timely results. Overcoming these challenges not only sharpens technical expertise but also positions you for advancement into senior or specialized technology roles.

What are the key skills and qualifications needed to thrive in the Data Crowd Compute position, and why are they important?

To thrive in a Data Crowd Compute role, you need a solid grounding in data analysis, familiarity with large-scale distributed computing, and a relevant technical degree or equivalent experience. Experience using cloud platforms like AWS, Azure, or Google Cloud, as well as tools such as Apache Hadoop or Spark, is highly valued. Strong problem-solving skills, attention to detail, and collaborative communication abilities help professionals excel in this position. These competencies are crucial for managing complex datasets, optimizing compute resources, and driving actionable insights within rapidly evolving data-driven environments.

What is a Data Crowd Compute?

A Data Crowd Compute job involves distributing computational tasks across a large number of people or devices to process data efficiently. This type of work is often used in AI training, data annotation, or large-scale problem-solving. Workers contribute by performing small tasks that collectively build a larger solution. These jobs are common in machine learning, crowdsourcing, and distributed computing projects.

More about Data Crowd Compute jobs
What cities are hiring for Data Crowd Compute jobs? Cities with the most Data Crowd Compute job openings:
What are the most commonly searched types of Data Crowd Compute jobs? The most popular types of Data Crowd Compute jobs are:
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What job categories do people searching Data Crowd Compute jobs look for? The top searched job categories for Data Crowd Compute jobs are:
Data Crowd Compute jobs near you
Infographic showing various Data Crowd Compute job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Solutions Architect, AI Factory Observability and Visualization - NVIS

Nvidia

Durham, NC • On-site

Full-time

Re-posted 13 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

NVIDIA's Infrastructure Specialists team is hiring a Senior Solutions Architect - AI Factory Observability & Visualization! This remote role develops full-spectrum visibility that supports the smooth functioning of HPC systems and AI factories, transforming intricate telemetry across network and compute into straightforward, actionable perspectives.

The role has a complete, end-to-end understanding of the HPC/AI system, running and interpreting microbenchmarks and workloads to confirm system readiness, then establishing the observability that maintains this state. The work involves collaborating across NVIDIA teams to help partners see, understand, and respond to HPC system and AI factory performance, from hardware to workload.

What You Will be Doing:

  • Run AI factory validation tools, microbenchmarks, and workloads provided by the team, and interpret results to assess system health and performance.

  • Gain a comprehensive understanding of the system from start to finish, including network topology, interconnects, and compute.

  • Establish what "healthy" represents across the stack - the metrics, logs, and signals that confirm a system is functioning well, and the thresholds that show it isn't.

  • Build and extend the telemetry surface across hardware, fabric, and workload, crafting how data is collected, transformed, stored, and surfaced.

  • Serve as the observability expert, investigating gaps in visibility to ensure it reflects true system behavior.

  • Develop automation (Python, Shell) for collecting, transforming, and presenting system and network data.

  • Recommend improvements to system visibility, data sources, and reporting that give teams clearer insight.

  • Collaborate with hardware, software, networking, datacenter, and product groups to ready HPC systems and AI factories for customer deployment, contributing documentation and readiness materials throughout the process.

What We Need to See:

  • Bachelor's degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or related field.

  • 6+ years of experience managing Linux-based systems in HPC, distributed systems, or large AI/ML settings.

  • Hands-on experience with the architecture of multi-GPU and/or multi-node clusters, including networking and interconnects.

  • Solid grasp of how HPC and AI factory systems fit together end to end, from network fabric through compute.

  • Proficiency with Python and Shell/Bash for scripting, automation, and tooling.

  • Practical experience working with observability systems (e.g., Prometheus, Grafana, Loki, or similar), including building custom exporters or collectors, setting up alerts, and handling metric cardinality and retention on a large scale.

  • Experience transforming metrics, logs, and traces into clear, actionable insight for complex distributed environments.

  • Familiarity with GPU and fabric telemetry (e.g., DCGM, NVLink, InfiniBand/Ethernet fabric counters) and using it to diagnose performance regressions.

  • Strong communication skills and the ability to work effectively with cross-functional teams.

Ways to Stand Out From the Crowd:

  • Experience with AI factory or large-scale AI infrastructure build, deployment, or operations.

  • Background in HPC systems engineering, SRE, or systems analysis for GPU-accelerated environments.

  • Experience building automation and data pipelines that feed dashboards and reporting at scale.

  • Demonstrated desire to use AI to solve practical problems, improve workflows, and guide data-driven decisions.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 28, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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