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    Senior Machine Learning Engineer - Philadelphia, United States - augmentjobs

    augmentjobs
    augmentjobs Philadelphia, United States

    2 weeks ago

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    Description

    Job Description

    Job Description

    Job Description:

    Responsibilities:

    1. Machine Learning Model Development: Lead the development and implementation of machine learning models to address complex business problems and opportunities. Design and experiment with algorithms, architectures, and techniques to optimize model performance and predictive accuracy.
    2. Data Preprocessing and Feature Engineering: Perform data preprocessing tasks, including data cleaning, transformation, and normalization, to prepare datasets for model training. Engineer relevant features from raw data to improve model performance and interpretability.
    3. Model Training and Evaluation: Train, validate, and optimize machine learning models using appropriate techniques and methodologies. Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, and ROC AUC, and iteratively refine models as needed.
    4. Deployment and Integration: Deploy machine learning models into production environments, integrating them into existing systems and workflows. Collaborate with software engineers and DevOps teams to ensure seamless deployment, monitoring, and maintenance of machine learning solutions.
    5. Cross-Functional Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders, to define project requirements, prioritize tasks, and deliver high-quality machine learning solutions.
    6. Technical Leadership: Provide technical leadership and guidance to junior team members, mentoring them in machine learning best practices, algorithms, and methodologies. Conduct code reviews, provide feedback, and help drive continuous improvement in the team's capabilities.
    7. Research and Innovation: Stay updated on the latest advancements in machine learning research, methodologies, and tools. Experiment with new techniques and technologies, conducting research and prototyping to explore novel solutions to complex problems.
    8. Documentation and Communication: Document machine learning models, algorithms, and workflows effectively, ensuring clear and concise documentation for reference and knowledge sharing. Communicate technical concepts and findings to non-technical stakeholders in a clear and understandable manner.

    Qualifications:

    1. Education: Bachelor's degree or higher in computer science, engineering, mathematics, statistics, or a related field. Advanced degrees (e.g., Master's or Ph.D.) in machine learning, artificial intelligence, or a related field are preferred but not required.
    2. Experience: Minimum of 5-7 years of experience in machine learning engineering, data science, or a related role, with a proven track record of leading and delivering complex machine learning projects from end to end.
    3. Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python, R, or Julia. Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.
    4. Machine Learning Techniques: Strong understanding of machine learning algorithms, techniques, and methodologies, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods.
    5. Data Skills: Solid data manipulation and analysis skills, including experience working with large datasets and performing data preprocessing tasks. Familiarity with SQL, NoSQL, and big data technologies (e.g., Hadoop, Spark) is a plus.
    6. Software Engineering: Knowledge of software engineering principles and best practices, including version control, code review, testing, and debugging. Experience with software development lifecycle (SDLC) methodologies such as Agile or Scrum.
    7. Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to understand complex problems, formulate solutions, and implement effective strategies to address them.
    8. Communication Skills: Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders. Ability to convey technical concepts and findings clearly and concisely.
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