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Machine Learning EngineerHalo Media • New York, New York, United States
Postuler Maintenant
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Machine Learning Engineer

Halo Media
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Postuler Maintenant

À propos

Halo Media is a digital innovation agency and precision engineering partner. For over 20 years, we’ve blended visionary design with technical depth to build scalable digital products for global brands and high-growth startups. With an AI-forward mindset and a focus on measurable outcomes, we help our clients move faster and smarter—turning complex business challenges into high-performance solutions.
Role Overview We are seeking hands-on Machine Learning Engineers for an urgent staff augmentation engagement ( 6-month temporary role, with a possibility of extension ). In this role, you will support, fix, and optimize a high-traffic, global consumer platform. Operating under the direct guidance of a Staff ML Architect, you will focus heavily on daily MLOps execution, pipeline maintenance, and ensuring models perform reliably in a production-grade environment.
Key Responsibilities
Model Lifecycle Management: Design, deploy, and monitor machine learning models within high-traffic environments, ensuring maximum reliability, performance, and scalability.
Pipeline Execution: Maintain, troubleshoot, and optimize end-to-end ML pipelines. Integrate tools like Spark and Airflow to streamline the flow from raw data ingestion through to offline and online model evaluation.
Daily MLOps: Execute daily model training and inference tasks. Build and manage automated containerized deployments to ensure smooth, continuous support for a major production platform.
Required Qualifications
Experience: 1–3 years of hands‑on experience in Machine Learning, MLOps, or Data Science within a professional environment.
GCP Expertise: Solid experience with the Google Cloud Platform ecosystem, particularly Vertex AI components (Workbench, Pipelines, Model Registry).
ML Frameworks & DevOps: Proficiency in modern ML frameworks (e.g., PyTorch ) and containerization tools ( Docker ) for automated builds.
Data Orchestration: Practical experience managing data processing flows using Apache Spark and Airflow .
Preferred Skills
Familiarity with real-time model serving and infrastructure (e.g., Triton Inference Server , Terraform ).
Previous experience in high-traffic, production‑grade environments.
Position Type 6 month temporary employee
Location Remote (Candidates must be based in the US or Canada )
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  • New York, New York, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

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