Machine Learning Engineer

Full Time
  • Full Time
  • Toronto

AMISEQ

Responsibilities:

  • Creates machine learning models and utilizes data to train models
  • Focuses on analyzing data to find relations between the input and the desired output Understands business objectives and develops models that help achieve them, along with metrics to track their progress
  • Designs and develops machine learning and deep learning systems
  • Runs machine learning tests and experiments
  • Implements appropriate machine learning algorithms


General Skills:

  • Experience managing available resources such as hardware, data, and personnel so that deadlines are met
  • Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability
  • Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
  • Experience verifying data quality, and/or ensuring it via data cleaning
  • Experience supervising the data acquisition process if more data is needed
  • Experience finding available datasets online that could be used for training Experience defining validation strategies
  • Experience defining the preprocessing or feature engineering to be done on a given dataset
  • Background in statistics and computer programming
  • A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experience

Experience and Skill Set Requirements

All requirements are ‘must have’:



(15%)

  • Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts, algorithms, and techniques.
  • Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models, especially BERT and other transformer-based models, for tasks like text classification, sentiment analysis, and language understanding.


(20%)

  • Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch.
  • Experience with implementing, training, and fine-tuning BERT models using these frameworks is crucial.


(30%)

  • Data Preprocessing Skills: Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
  • Programming Skills: Strong programming skills in Python, including experience with libraries like NumPy, Pandas, and Scikit-learn.


(20%)

  • Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.


(15%)

  • Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.

Source

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