Principal Machine Learning Engineer

Job Description:

Responsibilities:

1. Technical Leadership: Provide technical leadership and strategic guidance in the development and deployment of machine learning solutions. Define technical vision and roadmap for machine learning projects, driving innovation and excellence in machine learning technologies and methodologies.

2. Machine Learning Model Development: Lead the design, development, and implementation of advanced machine learning models and algorithms to address complex business problems and opportunities. Experiment with state-of-the-art techniques, architectures, and frameworks to push the boundaries of machine learning capabilities.

3. Research and Innovation: Stay updated on the latest advancements in machine learning research, methodologies, and tools. Lead research initiatives, collaborate with academia and industry partners, and prototype new solutions to address emerging challenges and opportunities in machine learning.

4. 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-impact machine learning solutions. Communicate technical concepts and findings effectively to non-technical stakeholders.

5. Model Deployment and Integration: Lead the deployment and integration of machine learning models into production environments, ensuring scalability, reliability, and performance. Collaborate with DevOps teams to implement best practices for model versioning, deployment, and monitoring.

6. Team Development and Mentorship: Mentor and coach junior and mid-level machine learning engineers, providing technical 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.

7. 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 strongly preferred.

2. Experience: Minimum of 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: Expertise 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: Deep understanding of 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. Proficiency in SQL, NoSQL, and big data technologies (e.g., Hadoop, Spark) is highly desirable.

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: Exceptional leadership and communication skills, with the ability to inspire, motivate, and lead a team of machine learning engineers. Strong problem-solving skills, strategic thinking, and ability to communicate technical concepts and findings clearly and concisely.