Data is based on 495 job openings.
The salary range for machine learning engineer is anywhere between $80,000 to $220,000 per year.
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 |
The salary range for entry-level machine learning engineer is between $80,000 and $110,000 per year.
The salary range for senior-level machine learning engineer is between $145,000 and $180,000 per year.
Yes, Machine Learning Engineer positions can be fully remote. Companies such as Trackunit, Weekday AI, ALTEN MÉXICO are hiring remotely for Machine Learning Engineer. Check out the latest remote Machine Learning Engineer job openings below.
Yes, there are 495 remote job openings:
You will be responsible for turning machine learning models into reliable, maintained production systems that support construction equipment operations. You will collaborate with data scientists and product teams to shape deployment patterns and ensure continuous model improvement.
You will own the end-to-end development of machine learning systems to extend the B2B data graph and infer buying intent from multilingual web content. This includes building and deploying LLM agents, optimizing model performance, and mentoring team members to raise engineering standards.
The role involves evaluating complex machine learning scenarios for technical accuracy and developing predictive statistical models. You will also be responsible for writing Python and SQL code while creating clear documentation for analytical concepts.
Develop and maintain machine learning models that influence production decision-making and business outcomes. Build and scale efficient ML pipelines for real-time and batch processing while monitoring model performance.
Develop and implement AI agents and automation to optimize internal engineering workflows and customer-facing delivery processes. Collaborate with cross-functional teams to identify high-leverage opportunities and integrate AI solutions into existing infrastructure.
You will design, build, and maintain the MLOps platform while automating the model lifecycle from notebook to production. Additionally, you will integrate streaming data pipelines and implement agentic workflows to ensure reliable, scalable ML systems.
You will collaborate with researchers to manage and execute a high volume of experiments while characterizing neural network performance. Additionally, you will document qualitative and quantitative results in technical reports and suggest process improvements for future research iterations.
