Machine Learning Infrastructure Engineer

  • Redwood City, CA
  • Posted 7 hours ago | Updated 7 hours ago

Overview

Hybrid
$165,000 - $210,000
Full Time

Skills

Artificial Intelligence
Analytics
Computer Vision
Deep Learning
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Python
Video
Real-time
Software Development
Cloud Computing
Computer Science

Job Details

ob Title: Machine Learning Infrastructure Engineer Computer Vision & AI

Location: Redwood City, CA (Hybrid Onsite 3 days/week)
Job Type: Contract-to-Hire or Full-Time
Pay Rate (Contract): $80 $90/hr (up to $100/hr for C2C)
Salary (Full-Time): $165K $210K

About the Role

We are seeking a highly skilled Machine Learning Infrastructure Engineer to join a cutting-edge AI team focused on real-time video analytics and computer vision. This role is ideal for someone passionate about building scalable ML systems and integrating the latest advancements in AI, including LLMs, LVMs, and Retrieval-Augmented Generation (RAG).

Key Responsibilities

  • Design and build robust machine learning infrastructure to support deep learning models on large-scale video data.
  • Develop scalable data engines for collecting, managing, and labeling training data.
  • Collaborate with research scientists to integrate state-of-the-art AI models into production systems.
  • Optimize infrastructure for continuous training, evaluation, and deployment of computer vision models.
  • Stay current with the latest trends in AI, ML, and computer vision to drive innovation.

Required Qualifications

  • 6+ years of industry experience (minimum 4 years in the U.S.)
  • BS/MS in Computer Science or related field
  • Strong foundation in machine learning, deep learning, and computer vision
  • Proven experience with scalable ML infrastructure and distributed systems
  • Proficient in Python and software development best practices
  • Experience with LLMs, LVMs, and RAG in production environments
  • Excellent communication and collaboration skills

Nice to Have

  • Experience working in real-time video processing environments
  • Familiarity with cloud-based ML platforms and MLOps tools

Interview Process

  • Initial phone/video screen
  • Onsite interview (includes technical deep dive and team fit)
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