Senior Machine Learning Platform Engineer-- SINDC5806817

Compunnel Inc.
Irvine, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Irvine, United States of America

Tech stack

Java
Artificial Intelligence
Amazon Web Services (AWS)
Data analysis
Artificial Neural Networks
Azure
Cloud Computing
Cloud Engineering
Computer Programming
Continuous Integration
Distributed Systems
Generalized Linear Model
Python
Machine Learning
Open Source Technology
Azure
Software Engineering
Data Ingestion
Random Forest
Deep Learning
Containerization
Kubernetes
Information Technology
XGBoost
Machine Learning Operations
Data Pipelines
Docker
Databricks
Microservices

Job description

  • 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.

Requirements

  • 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.

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