Machine Learning Systems Engineer, Networking

About the role

Join our team of innovative engineers who are 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'll 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 where needed: 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

A BS (or equivalent experience) and 5+ years of experience, MS and 3+ years, or PhD with 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

Qualifications

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

Skills

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

Benefits

Competitive salaries and a generous benefits package

Exclusive engineering teams rapidly growing due to unprecedented growth

Pay

Base salary range: $152,000 - $241,500 for Level 3, and $184,000 - $287,500 for Level 4

Schedule

Not specified