Lead Machine Learning Engineer

3 weeks ago

Philadelphia, United States augmentjobs Full time
Job DescriptionJob Description

Job Description:

Responsibilities:

  1. Technical Leadership: Lead machine learning projects from conception to completion, providing technical guidance, direction, and mentorship to a team of machine learning engineers. Set technical direction, define project objectives, and ensure alignment with organizational goals and priorities.

  2. Machine Learning Model Development: Design, develop, and implement cutting-edge machine learning models and algorithms to solve complex business problems and drive innovation. Experiment with different techniques, architectures, and frameworks to optimize model performance and scalability.

  3. Data Strategy and Management: Define data strategy and requirements for machine learning projects, including data collection, preprocessing, feature engineering, and labeling. Collaborate with data engineering teams to ensure access to high-quality, relevant, and well-curated datasets.

  4. Model Deployment and Integration: Lead the deployment and integration of machine learning models into production environments, collaborating with software engineering and DevOps teams to ensure seamless integration, monitoring, and maintenance. Implement best practices for model versioning, deployment, and scaling.

  5. Cross-Functional Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders, to understand requirements, prioritize tasks, and deliver high-quality machine learning solutions. Communicate technical concepts and findings effectively to non-technical stakeholders.

  6. Research and Innovation: Stay updated on the latest advancements in machine learning research, methodologies, and tools. Lead research initiatives, conduct experiments, and prototype new solutions to address emerging challenges and opportunities in machine learning.

  7. Team Development and Growth: Mentor and coach junior team members, providing guidance, feedback, and support to help them grow and develop their skills. Foster a culture of learning, collaboration, and innovation within the machine learning team.

  8. Documentation and Best Practices: Establish and promote best practices, standards, and guidelines for machine learning development, documentation, and deployment. Document machine learning models, algorithms, and workflows effectively for reference and knowledge sharing.

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 7-10 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. Extensive experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.

  4. Machine Learning Techniques: Expertise in a wide range of machine learning algorithms, techniques, and methodologies, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods.

  5. Data Skills: Strong 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. Leadership and Communication Skills: Strong leadership and communication skills, with the ability to inspire, motivate, and lead a team of machine learning engineers. Effective communication and collaboration skills, with the ability to convey technical concepts and findings clearly and concisely.

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