Machine Learning Infrastructure Engineer

Cognizant

Confirmed live today High trust
Remote

Quick summary

Work type
Remote
Location
San Francisco, CA
Salary
$88,000–$155,000 / yr
Posted
11 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $223k
This role $122k
$65k $300k
below market most similar roles pay here above market

This role pays less than 98% of similar roles. Most pay $192,050–$254,750 — the blue band above. At the midpoint, this role pays about $122k versus about $223k for comparable roles.

Based on 240 similar postings.

Employer

About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 330 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 211 roles with salary data.

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At a glance

TL;DR · Machine Learning Infrastructure Engineer

The Staff Machine Learning Infrastructure Engineer will architect and lead the technical vision for ML platform initiatives, focusing on building scalable infrastructure to power ML capabilities. You will design and drive the evolution of data systems, feature management, and model serving infrastructure to support diverse ML workloads. Key responsibilities include building scalable data processing systems, establishing best practices for ML observability, and leading cross-functional technical initiatives. You will utilize expertise in Spark, Beam, TorchServe, Tensorflow Serving, and Triton. The role requires deep knowledge of distributed systems, MLOps practices, container orchestration, and cloud platforms. You will solve complex problems related to inference optimization, dataset versioning, lineage tracking, and resource management to support critical business applications across the entire ML lifecycle.

What you'll do

  • Architect end-to-end ML infrastructure covering data processing, feature management, and model serving.
  • Design and implement next-generation ML platforms to support diverse ML workloads.
  • Build scalable data processing systems and feature platforms for massive-scale workloads.
  • Design robust ML serving architectures for both real-time and batch inference.
  • Establish best practices for ML observability, monitoring, and operational excellence.
  • Drive infrastructure decisions and standardization across the entire ML lifecycle.
  • Lead cross-functional technical initiatives and mentor platform engineers.

What we're looking for

  • 10+ years of software engineering experience.
  • 5+ years of experience focusing on ML infrastructure and GCP.
  • Deep expertise in distributed systems and data processing at scale.
  • Strong background in ML platform development and MLOps practices.
  • Expert-level knowledge in Spark, Beam, feature stores, and ML serving platforms like TorchServe, Tensorflow Serving, or Triton.
  • Experience building production ML infrastructure, data pipelines, and monitoring systems.
  • Experience with GPU infrastructure and optimization (preferred).
  • Master’s degree in computer science or related field (preferred).

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