Zurich, Switzerland Today Not disclosed
Machine Learning & AI Linux Jobs
Machine learning workloads run almost exclusively on Linux. From GPU cluster management and distributed training pipelines to model serving infrastructure and MLOps tooling, Linux engineering skills are essential across the entire AI stack. Browse ML and AI engineering roles at research labs, AI-native startups, and enterprise teams building production machine learning systems.
Santa Clara, California, United States Yesterday Not disclosed
Pangyo, South Korea 5d ago Not disclosed
Hawthorne, California, United States 5d ago $100k – $135k
Tel Aviv, Israel 6d ago Not disclosed
AS
Shanghai, China 6d ago Not disclosed
Remote, United States May 27 $128k – $213k
San Francisco, California, United States May 27 Not disclosed
Remote, United States May 27 $150k – $250k
Foster City, California, United States May 26 Not disclosed
Foster City, California, United States May 26 Not disclosed
Foster City, California, United States May 26 Not disclosed
Foster City, California, United States May 26 Not disclosed
Foster City, California, United States May 26 Not disclosed
Foster City, California, United States May 26 Not disclosed
DI
Bengaluru, Karnataka, India May 26 Not disclosed
IL
New York or Remote, United States May 22 Not disclosed
Austria May 22 Not disclosed
Cambridge, United Kingdom May 22 Not disclosed
London, United Kingdom May 22 Not disclosed
Frequently Asked Questions
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Core Linux skills for ML roles include GPU driver management (CUDA, ROCm), containerisation with Docker and Kubernetes, distributed systems fundamentals, Python environment management, and familiarity with HPC job schedulers like Slurm. MLOps roles additionally require infrastructure-as-code skills (Terraform, Ansible) and experience with ML platforms such as Kubeflow, MLflow, or Ray.
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MLOps (machine learning operations) covers the infrastructure, tooling, and processes needed to deploy, monitor, and maintain ML models in production. Since model training runs on Linux-based GPU clusters and production inference typically runs in Linux containers on Kubernetes, strong Linux skills are a prerequisite. MLOps engineers are responsible for building the pipelines that connect data, training, and serving.
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Machine learning engineers are among the highest-paid software professionals. In the United States, salaries typically range from $140,000 to $220,000 at established tech companies, with AI-native startups and research labs sometimes offering higher total compensation including equity. MLOps and AI infrastructure roles typically range from $120,000 to $180,000 depending on seniority and location.
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Research scientist roles at top AI labs often require or strongly prefer a PhD. However, the majority of ML engineering and MLOps positions do not, they prioritise strong engineering skills, practical experience with ML frameworks (PyTorch, TensorFlow), and system-level knowledge. Many successful ML engineers transition from software engineering or DevOps backgrounds.