Machine Learning Systems Engineer, Networking

Details

Join a team building an AI Data Center AIOps platform that turns raw, high-volume telemetry into reliable, job-centric insights and automation for GPU fleets. As an ML Engineer on this team, you will design and implement ML algorithms that run in real-time streaming pipelines, detecting anomalies and surfacing insights across massive-scale infrastructure before they impact AI training and inference.

Responsibilities

• Implement production ML algorithms in Go optimized for real-time streaming pipelines operating at massive scale under strict resource constraints.
• Design and develop new ML algorithms: anomaly detection, health scoring, and predictive analytics on high-volume time-series telemetry from GPU and network infrastructure.
• Improve and extend existing algorithms and experiment with new approaches suited to real-time streaming constraints.
• Build and maintain end-to-end ML pipelines — from data ingestion and schema design through model inference — optimized for on-premises, latency-sensitive deployments.
• Partner with the Data Science team on algorithm design, prototype evaluation, and translating research findings into platform requirements.

Requirements

• BS (or equivalent experience) + 5+ years, MS + 3+ years, or PhD + 1+ years in Computer Science, Statistics, or a related field.
• Strong mathematical foundation: statistics, probability, linear algebra, and algorithm analysis.
• Proven experience implementing and optimizing ML algorithms in production — this is a coding-first role; strong implementation skills are required.
• Strong programming skills in one or more of Go, C/C++, Rust, or Scala; Python working knowledge is a plus.
• Familiarity with time-series databases and streaming data architectures.
• Ability to work independently and navigate ambiguity in a fast-paced engineering environment.

Ways to stand out

• Data science background with hands-on experience building and validating ML models — bridging research and production implementation.
• Experience implementing ML algorithms directly in systems languages for latency-sensitive or resource-constrained environments.
• Research experience: knowing the latest ML literature and translating advances into practical improvements.
• Experience with Kafka-based streaming pipelines and real-time feature engineering at scale.

Compensation & Benefits

• The posting states: "With competitive salaries and a generous benefits package." You will also be eligible for equity and benefits.
• Base salary ranges (by level): Level 3: 152,000 USD - 241,500 USD; Level 4: 184,000 USD - 287,500 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

Additional information

• Applications for this job will be accepted at least until May 22, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is an equal opportunity employer and states a commitment to fostering a diverse work environment.