Machine Learning Infrastructure Specialist

3 weeks ago

California, United States Stealth Full time
About Stealth

We are a pioneering AI company dedicated to developing cutting-edge machine learning solutions that drive innovation across industries. Our mission is to empower data scientists and engineers to build and deploy high-performance models at scale.

Key Responsibilities
  • Design and maintain scalable machine learning infrastructure to support large-scale data processing, model training, and deployment.
  • Optimize ML workflows, from data ingestion to feature engineering, model training, and model serving in production.
  • Collaborate with data scientists, ML engineers, and DevOps teams to ensure seamless integration and deployment of models in a production environment.
  • Automate and manage ML pipelines, ensuring continuous training and monitoring of models with minimal manual intervention.
  • Ensure that infrastructure supports rapid experimentation, tuning, and evaluation of new models.
  • Stay up-to-date with the latest cloud technologies and best practices to improve performance and reliability of ML systems.
Qualifications
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field from a top-tier institution.
  • 2-4 years of experience in designing and managing machine learning infrastructure.
  • Strong experience with cloud platforms (AWS, GCP, or Azure), including knowledge of managed ML services like SageMaker, Vertex AI, or Azure ML.
  • Proficiency in Python, with experience in ML libraries (TensorFlow, PyTorch, Scikit-learn).
  • Experience with containerization technologies (e.g., Docker, Kubernetes) and microservice architecture for deploying scalable ML models.
  • Strong knowledge of MLOps principles and tools (e.g., MLflow, Kubeflow, Airflow).
  • Experience with distributed computing frameworks (e.g., Apache Spark, Ray) and parallelization for large-scale model training.
  • Knowledge of continuous integration/continuous deployment (CI/CD) best practices in ML environments.
Preferred Skills
  • Experience with data versioning and experiment tracking tools (e.g., DVC, Weights & Biases).
  • Background in managing high-performance computing resources (GPUs, TPUs) for training deep learning models.
  • Familiarity with modern database systems and data lakes (e.g., Snowflake, Delta Lake) for large-scale data management.
  • Experience with security and compliance for ML systems, ensuring data privacy and regulatory adherence.
  • Strong problem-solving skills and ability to work effectively in a fast-paced, dynamic environment.
Why Stealth?
  • Lead the development of cutting-edge ML infrastructure that powers AI solutions with global impact.
  • Work alongside a world-class team of engineers and data scientists at the forefront of AI technology.
  • Competitive salary and benefits package, with opportunities for career growth.
  • Flexible, remote work environment focused on innovation, collaboration, and work-life balance.

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