Key Responsibilities:
Team Leadership: Manage and mentor a team of four ML engineers, fostering a collaborative environment that encourages innovation and professional growth.
Application Development: Oversee the design and development of reference applications in computer vision and LLMs, ensuring they meet industry standards and client requirements.
Pipeline Engineering: Develop and optimize ML pipelines for vision and LLM applications, facilitating seamless integration and deployment for customers and internal assessments.
Performance Evaluation: Lead efforts to benchmark our hardware/platform's performance against competitors, providing insights and recommendations for continuous improvement.
Cross-Project Coordination: Ensure alignment and effective communication among team members, synchronizing efforts across various application projects.
Stakeholder Collaboration: Work closely with product managers, hardware engineers, and other stakeholders to align application development with business objectives and technological advancements.
Qualifications:
Educational Background: Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.
Experience: Minimum of 3 years leading ML teams in a production environment, with a focus on computer vision and LLM applications.
Technical Proficiency: Strong programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch.
Pipeline Development: Proven experience in designing and implementing ML pipelines to deployment from open-source examples.
Benchmarking Expertise: Familiarity with performance evaluation techniques and tools for assessing hardware and software platforms.
Leadership Skills: Demonstrated ability to lead, mentor, and coordinate a team of engineers, ensuring project alignment and timely delivery.
Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to diverse stakeholders.
Preferred Qualifications:
Industry Knowledge: Understanding of the competitive landscape in ML hardware and software platforms.
Project Management: Experience in managing multiple projects simultaneously, with a track record of successful project delivery.
Continuous Learning: Commitment to staying updated with the latest advancements in ML technologies and applications.
