Talantix.io
Hugging Face•04.03.26
AI SCORE 8.5

Cloud Machine Learning Engineer - Remote

$120K–$150K/year
RemoteAI/Big Data
Python•AWS•GCP•MLOps•PyTorch•TensorFlow•Docker•Data Engineering

About the Role

We are seeking a talented Cloud Machine Learning Engineer - Remote to join our innovative team at Hugging Face. In this role, you will leverage your expertise in machine learning and cloud technologies to develop and deploy end-to-end ML systems that enhance our AI products. You will work closely with cross-functional teams to create scalable solutions that impact the AI community.

What You'll Do

  • Design and implement cloud-based machine learning solutions using AWS and GCP.
  • Collaborate with data engineers to optimize data architecture and pipelines for ML workflows.
  • Develop and maintain ML models using frameworks such as PyTorch and TensorFlow.
  • Ensure the reliability and scalability of ML systems in production environments.
  • Participate in code reviews and contribute to best practices in MLOps.
  • Support the ML/AI community through open-source contributions and knowledge sharing.

Requirements

  • 3+ years of experience as a Machine Learning Engineer or similar role.
  • Proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow).
  • Strong understanding of cloud platforms, particularly AWS and GCP.
  • Experience with Docker and container orchestration.
  • Familiarity with data engineering concepts and tools.
  • Excellent problem-solving skills and the ability to work independently in a remote setting.

Nice to Have

  • Experience with Node.js and TypeScript.
  • Knowledge of computer vision and natural language processing.
  • Familiarity with legal aspects related to IP and privacy in AI.

What We Offer

  • Flexible working hours and remote options.
  • Health, dental, and vision benefits for employees and dependents.
  • Parental leave and flexible paid time off.
  • Reimbursement for relevant conferences, training, and education.
  • Opportunity to visit office spaces in NYC and Paris.
  • Support for the ML/AI community.
Why This Job8.5 of 10

This role offers a unique opportunity to work with cutting-edge technologies in a fully remote setting. Hugging Face is known for its innovative approach to AI and machine learning.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

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Proficient in deploying machine learning models using AWS and GCP services, particularly in serverless architectures and containerization with Docker for scalable deployments.

→

Self-motivated and disciplined in a remote work environment, demonstrating the ability to manage time effectively and collaborate asynchronously with cross-functional teams across different time zones.

→

Strong foundation in MLOps practices, including CI/CD pipelines for machine learning, with hands-on experience in frameworks like PyTorch and TensorFlow for model development and optimization.

Learning Resources

→Python for Data Science and Machine Learning Bootcampcourse
→AWS Machine Learning Documentationguide
→MLOps: Continuous Delivery and Automation Pipelines in Machine Learningbook

Career Path

Cloud Machine Learning Engineer - Remote(Now)→Senior Cloud Machine Learning Engineer(1-2 years)→Lead Machine Learning Architect(3-5 years)

Market Overview

Market Size 2024
$49.5B
Annual Growth
20.1%
AI Adoption
65%
Investment in Cloud ML
+35%
Labour Demand for ML Engineers
+25%
Avg Salary for Cloud ML Engineer
$130K

Skills & Requirements

Required
PythonAWSGCP
Growing in Demand
KubernetesApache AirflowNatural Language Processing (NLP)
Declining
MapReduceHadoop

Domain Trends

Increased Demand for Real-time Data Processing
With 72% of businesses prioritizing real-time analytics, skills in tools like Apache Kafka are becoming essential.
Shift Towards Serverless Architectures
Over 60% of organizations are adopting serverless computing on platforms like AWS Lambda, reducing infrastructure management needs.
Focus on Ethical AI Practices
Around 55% of companies are implementing ethical guidelines for AI, creating a demand for expertise in responsible ML practices.

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