Design, build, and deploy industrial-level machine learning models for ad ranking, bidding, and optimization. Manage the full ML lifecycle from research and feature engineering to scalable serving and production monitoring.
Remote Machine Learning Engineer Salary – updated July 2026
The average salary for remote machine learning engineer is $140,000 per year. This is based on data from 486 job openings.
Where do you get the data from?
Data is based on 486 job openings.
How much does a Machine Learning Engineer make?
The salary range for machine learning engineer is anywhere between $80,000 to $220,000 per year.
Can you provide a breakdown of salary data by years of experience?
Below is a breakdown of salary data by years of experience:
| EXPERIENCE | MINIMUM SALARY | AVERAGE SALARY | MAXIMUM SALARY |
|---|---|---|---|
| Entry Level Machine Learning Engineer (0-1 years) | $80,000 | $95,000 | $110,000 |
| Mid Level Machine Learning Engineer (2-4 years) | $110,000 | $127,500 | $145,000 |
| Senior Level Machine Learning Engineer (5-9 years) | $145,000 | $162,500 | $180,000 |
| Lead Level Machine Learning Engineer (10+ years) | $180,000 | $200,000 | $220,000 |
* Data is based on 486 job openings.
What is the salary range for remote Entry-Level Machine Learning Engineer?
The salary range for entry-level machine learning engineer is between $80,000 and $110,000 per year.
What is the salary range for remote Senior-Level Machine Learning Engineer?
The salary range for senior-level machine learning engineer is between $145,000 and $180,000 per year.
Can Machine Learning Engineer be fully remote?
Yes, Machine Learning Engineer positions can be fully remote. Companies such as Reddit, Shuru Technologies Private Limited, Pivotal Solutions, Inc. are hiring remotely for Machine Learning Engineer. Check out the latest remote Machine Learning Engineer job openings below.
Are there any remote job openings for Machine Learning Engineer?
Yes, there are 486 remote job openings:
Machine Learning Engineer
Shuru Technologies Private Limited
·
Full Time
·
5 Days Ago
Shuru Technologies Private Limited
The MLOps Engineer will design, build, and maintain scalable machine learning pipelines and cloud-based environments. They will collaborate with cross-functional teams to deploy, monitor, and govern enterprise ML solutions to drive business impact.
The role involves designing, developing, and deploying scalable machine learning models to solve real-world problems. The engineer will collaborate with cross-functional teams to integrate these models into production systems and optimize them for performance.
Design and operate cloud-native data and ML infrastructure to enable real-time intelligence from raw events. Architect scalable data pipelines, feature stores, and reproducible ML workflows while partnering with cross-functional teams to ship resilient services.
Design and deploy agentic LLM solutions to detect and prevent fraud, abuse, and security threats at scale. Collaborate with cross-functional teams to integrate these systems into production and identify product vulnerabilities.
Design and deploy agentic LLM solutions to detect and prevent fraud, abuse, and security threats at scale. Build production-grade ML systems on cloud platforms and collaborate with cross-functional teams to safeguard the GitHub platform.
Develop and implement AI/ML techniques and architectures to transform large datasets into meaningful business information. Create functional prototypes and custom scalable solutions using cloud services to drive organizational innovation.
Develop and scale AI-powered experiences focusing on NLP and Large Language Models. Design evaluation frameworks and safety guardrails to ensure model reliability and performance in production.
Architect and maintain high-performance backend systems and MLOps pipelines for AI simulations at scale. Develop responsive frontends and optimize data infrastructure to support synthetic data generation and model evaluation.
Design and implement enterprise machine learning products, platforms, and pipelines to meet organizational objectives. Collaborate with stakeholders and cross-functional teams to translate business requirements into technical ML solutions and analyze model performance.
