Real-Time ML Systems Engineer — Anomaly Detection & Streaming

NVIDIA; Santa Clara (CA); On-site; USD 152,000 - 287,500; Full time

Job summary

NVIDIA seeks an ML Engineer to design and implement real-time ML algorithms that process millions of telemetry streams for an AI Data Center AIOps platform. You will build production-grade models and pipelines that detect anomalies and surface insights under tight CPU and memory budgets.

You’ll code in Go, C/C++, Rust or Scala, collaborate with Data Science to translate research into platform requirements, and contribute to scalable on‑premises deployments.

Qualifications

• BS (or equivalent) in Computer Science, Statistics, or related field with 5+ years experience; MS with 3+ years; or PhD with 1+ year.
• Strong foundation in statistics, probability, linear algebra and algorithms.
• Production ML experience; strong coding skills in one of Go, C/C++, Rust, or Scala.

Responsibilities

• Implement production ML algorithms in Go for real-time streaming pipelines.
• Design and develop anomaly detection, health scoring, and predictive analytics on high-volume telemetry.
• Improve and extend existing ML algorithms for latency-sensitive deployments.
• Build end-to-end ML pipelines from data ingestion to model inference for on-premises systems.
• Collaborate with Data Science to translate research into platform requirements.

Skills

Go

C/C++

Rust

Scala

Python

Education

Bachelor's degree in CS/Math/related field

MS in CS/Statistics

PhD in CS/Statistics

Tools

Kafka

Time-series databases

Streaming frameworks

Job description

NVIDIA seeks an ML Engineer to design and implement real-time ML algorithms that process millions of telemetry streams for an AI Data Center AIOps platform. You will build production-grade models and pipelines that detect anomalies and surface insights under tight CPU and memory budgets.

You’ll code in Go, C/C++, Rust or Scala, collaborate with Data Science to translate research into platform requirements, and contribute to scalable on‑premises deployments.