Senior Machine Learning Platform Engineer-- SINDC5806817

Senior Machine Learning Platform Engineer-- SINDC5806817 at Compunnel Inc. Senior Machine Learning Platform Engineer-- SINDC5806817 at Compunnel Inc. in Aliso Viejo, California Posted in about 20 hours ago.

Type:

full-time

Job Description:

Job:

Senior Machine Learning Platform Engineer Location:

Irvine, CA Onsite Job Description What you'll do Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring. Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry. Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar. Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML. Partner with Data Engineers to build robust data pipelines for model-ready datasets. Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions. Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap. Ensure compliance with security, privacy, and regulatory requirements throughout the ML lifecycle. What you'll bring Required Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement. Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure. Expertise in building large-scale distributed systems and microservices. Strong programming skills in Python, Go, or Java. Experience with containerization and orchestration (e.g., Docker, Kubernetes). Advanced experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks. Cloud platform experience (AWS, GCP, or Azure). Experience with statistical learning algorithms (GLM, XGBoost, Random Forest) and deep learning (neural networks, transformers). Strong communication, leadership, and problem-solving skills.