Santa Clara, California, United States Yesterday $30 – $59
Linux Machine Learning & AI Engineering Jobs
Machine learning engineers build, train, and deploy AI models in production. The ML stack (CUDA, PyTorch, TensorFlow, Kubernetes-based training clusters) runs exclusively on Linux. MLOps roles focus on the infrastructure and tooling that makes model development and deployment repeatable and scalable. AI infrastructure demand is booming with the rise of large language models and generative AI.
San Francisco, California, United States 2d ago $189k – $274k
Pangyo, South Korea 5d ago Not disclosed
Hawthorne, California, United States 5d ago $100k – $135k
Austria May 22 Not disclosed
Cambridge, United Kingdom May 22 Not disclosed
Bristol, United Kingdom May 22 Not disclosed
London, United Kingdom May 22 Not disclosed
Paris, France May 21 Not disclosed
Paris, France May 21 Not disclosed
Seattle, Washington, United States May 21 Not disclosed
Menlo Park, California, United States May 21 Not disclosed
Singapore, Singapore May 21 Not disclosed
Bengaluru, Karnataka, India May 21 Not disclosed
Irvine, California, United States May 21 Not disclosed
Palo Alto, California, United States May 21 $170k – $205k
San Jose, California, United States May 21 Not disclosed
P
Remote, EMEA/East Coast May 21 Not disclosed
P
Remote, EMEA/East Coast May 21 Not disclosed
P
Remote, EMEA May 21 Not disclosed
Frequently Asked Questions
-
ML engineers productionise machine learning models: writing training pipelines, serving infrastructure, feature stores, and monitoring systems. They work at the intersection of software engineering and data science, ensuring models run reliably, efficiently, and at scale in production environments.
-
GPU computing for ML requires CUDA, which is developed and optimised for Linux. Training clusters run on Linux nodes. Container-based training (Docker, Singularity) and orchestration (Kubernetes, Slurm) are Linux-native. Deep Linux knowledge (NUMA, huge pages, GPU drivers, storage I/O) directly impacts training performance.
-
MLOps applies DevOps principles to machine learning: automated model training pipelines, experiment tracking (MLflow, Weights & Biases), model registries, CI/CD for models, A/B testing infrastructure, and production monitoring. MLOps engineers build the platforms that make ML development more systematic and scalable.
-
ML engineers command premium salaries. Senior ML engineers in the US earn $160,000–$220,000 base, with total compensation at AI-focused companies (OpenAI, Anthropic, Google DeepMind) commonly reaching $300,000+. Demand far exceeds supply, particularly for engineers with production experience.