Senior Machine Learning Engineer

Senior Machine Learning Engineer Job Description Template

Our company is looking for a Senior Machine Learning Engineer to join our team.

Responsibilities:

  • Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”;
  • Partner with data scientists to understand, implement, refine and design machine learning and other algorithms;
  • Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models;
  • Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models;
  • Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver;
  • Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.

Requirements:

  • Semiconductors / Electronics;
  • Knowledge of data query and data processing tools (i.e. SQL);
  • Understand machine learning principles (training, validation, etc.);
  • Experience using deep learning architectures;
  • Experience with GPU acceleration (i.e. CUDA and cuDNN);
  • Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering);
  • Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP);
  • BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience;
  • Mathematics fundamentals: linear algebra, calculus, probability;
  • Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code;
  • Minimum experience of at least 5 years on relevant technologies;
  • Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark);
  • Experience deploying highly scalable software supporting millions or more users;
  • Interest in reading academic papers and trying to implement state-of-the-art experimental systems;
  • Industry.