© Bigabid

Careers at Bigabid

Senior Deep Learning Engineer

#li-hybrid Bigabid focuses on solving the key challenge of growth for mobile apps by building Machine Learning and Big Data-driven technology that can both accurately predict what apps a user will like and connect them in a compelling way. Our technology operates at a scale well beyond some of the largest internet companies, processing over 50 TB of raw data per day, handling over 4 million requests per second, and interacting with over a billion unique users a week. 

Our innovative platform is leading-edge with a strong product-market fit. As a result, we're seeing remarkable growth and close to zero customer churn. To support our hyper-growth and continue propelling the growth of some of the biggest names in the mobile industry, we offer a wide range of opportunities for different skill levels and experiences.

We are looking for a Senior ML Engineer who is passionate about bringing models from development to production. As a Senior Machine Learning Engineer, you will join a team that specializes in the development and deployment of machine learning models at scale. Your role will be pivotal in exploring and integrating new technologies related to machine learning, artificial intelligence, and large-scale data processing.

Responsibilities

  • Leading the development of machine learning pipelines that are scalable, ensuring they can accommodate future growth in data volume and diversity.
  • Actively participating in the selection and implementation of new technologies and architectural components to advance our machine learning capabilities.
  • Develop ML solutions using advanced DL techniques to a diverse set of problems.
  • Collaborating with cross-functional teams to integrate machine learning solutions into the broader system architecture

Requirements:

  • +5 years of proven experience as a Data Engineer or Machine Learning Engineer
  • +2 years experience working with Deep Learning.
  • Strong experience in developing, deploying, and maintaining machine learning models in a production environment.
  • Construct and maintain data pipelines for model training and serving.
  • Experience with cloud-native architectures, preferably AWS.
  • Experience in recommendations, ad-tech, or highly imbalanced data- an advantage
  • Experience with Embeddings - an advantage
  • Experience with real-time inference - an advantage
  • Experience with GPU - an advantage
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