Data is based on 507 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 |
| Junior 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 Atlassian, ADT, Capgemini are hiring remotely for Machine Learning Engineer. Check out the latest remote Machine Learning Engineer job openings below.
Yes, there are 507 remote job openings:
You will develop and implement cutting-edge machine learning algorithms and train models to integrate AI functionalities into Atlassian products. Your role involves designing system architectures, conducting experiments, and collaborating with cross-functional teams to deliver AI-driven features.
The Machine Learning Engineer will design, build, and deploy production-grade machine learning models and AI systems within the ADT Data & AI organization. This role involves operationalizing AI algorithms, automating model retraining cycles, and ensuring intelligence systems are secure, scalable, and highly available.
Build and deploy scalable machine learning solutions and Generative AI applications to drive business outcomes. Collaborate with data engineering and business teams to transition models from experimentation to production environments.
The engineer will develop, train, and operationalize predictive and classification models for production applications. They will also manage data preparation, feature engineering, and integrate model inference into application services.
Design, build, and maintain data pipelines and ETL processes, using Azure services such as Azure Databricks to transform raw data into reliable, secure, high-quality data. Implement MLOps practices, monitor and troubleshoot pipelines, maintain data lineage and documentation, and collaborate with data scientists, engineers, and software developers.
Design, build, and maintain scalable machine learning models to automate logistics processes. Own end-to-end projects from data exploration to production deployment and monitoring.
Design and implement machine learning models for time-series and sequential data to extract signals from noisy sensor inputs. Develop and optimize real-time inference pipelines while collaborating with cross-functional teams to bring research models into production.
You will manage the full lifecycle of machine learning models, from data exploration and training to deployment and monitoring on AWS SageMaker. Additionally, you will collaborate with cross-functional teams to ensure model quality, explainability, and performance in production environments.
You will manage the full lifecycle of DS/ML solutions, from designing ETL pipelines and developing ranking models to deploying production-ready microservices. Additionally, you will collaborate with business stakeholders to design A/B tests and identify product growth opportunities.
Design, build, and deploy production-grade machine learning models and systems at scale to power core platform experiences. Own the full ML lifecycle from problem definition and feature engineering to training, evaluation, deployment, and monitoring.
