Talantix.io
Coinbase•09.03.26
AI SCORE 8.5

Staff Machine Learning Engineer - Core Automation Platform

$218K–$257K/year
RemoteCrypto/Web3
Python•TensorFlow•PyTorch•AWS•Azure•Data Analysis•Machine Learning

About the Role

Coinbase is hiring a full-time Staff Machine Learning Engineer - Core Automation Platform to join our innovative team. This Staff Machine Learning Engineer remote position allows you to work from anywhere in the United States, contributing to the development of cutting-edge machine learning solutions that enhance our platform's automation capabilities.

What You'll Do

  • Design and implement scalable machine learning models that drive automation across our core platform.
  • Collaborate with cross-functional teams to integrate machine learning solutions into existing systems.
  • Analyze large datasets to extract insights and improve model performance.
  • Lead the development of best practices for machine learning engineering and deployment.
  • Mentor junior engineers and contribute to their professional growth.

Requirements

  • 5+ years of experience as a Machine Learning Engineer or in a similar role.
  • Strong proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch.
  • Experience with cloud platforms like AWS or Azure.
  • Solid understanding of data structures, algorithms, and software engineering principles.
  • Excellent problem-solving skills and ability to work in a fast-paced environment.

Nice to Have

  • Experience in the crypto or blockchain industry.
  • Knowledge of reinforcement learning techniques.
  • Familiarity with containerization technologies like Docker.

What We Offer

  • Competitive salary ranging from $218,025 to $256,500.
  • Flexible working hours and a fully remote work environment.
  • Comprehensive health benefits and wellness programs.
  • Generous paid time off and holiday leave.
  • Opportunities for professional development and continuous learning.
Why This Job8.5 of 10

This Staff Machine Learning Engineer role at Coinbase offers a competitive salary, remote work flexibility, and the opportunity to lead innovative automation projects in the crypto industry.

Salary Range
Required
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Optional
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Bonus
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Who Will Succeed Here

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Proficient in Python with extensive experience in machine learning frameworks such as TensorFlow and PyTorch, enabling the design and implementation of complex ML models tailored for automation.

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Self-motivated and disciplined to thrive in a remote work environment, demonstrating strong time management and independent problem-solving skills to meet project deadlines without direct supervision.

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Deep understanding of cloud services like AWS and Azure for deploying machine learning solutions, with a strong analytical mindset to leverage data analysis for continuous improvement of automation processes.

Learning Resources

→Deep Learning with Python and PyTorchcourse
→Python for Data Analysisbook
→Machine Learning Crash Course by Googleguide

Career Path

Staff Machine Learning Engineer - Core Automation Platform(Now)→Lead Machine Learning Engineer(1-2 years)→Director of Machine Learning(3-5 years)

Market Overview

Market Size 2024
$15.2B
Annual Growth
23.5%
AI Adoption in Enterprises
80%
Investment in ML Technologies
+45%
Labour Demand for ML Engineers
+35%
Avg Salary for Senior ML Engineers
$150K

Skills & Requirements

Required
PythonTensorFlowPyTorch
Growing in Demand
Deep LearningNatural Language Processing (NLP)MLOps
Declining
R ProgrammingMATLAB

Domain Trends

Increased Use of Automated Machine Learning (AutoML)
Organizations are adopting AutoML tools to reduce the time and expertise required for model development, with a projected growth rate of 30% in usage by 2025.
Integration of ML with Edge Computing
The demand for deploying machine learning models on edge devices is increasing, with 25% of companies planning to implement edge solutions by 2025.
Focus on Ethical AI and Bias Mitigation
Companies are prioritizing ethical considerations in AI, with 70% of organizations reporting initiatives to address bias in machine learning models by the end of 2024.

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