Principal ML Engineer - AI Research (Remote)

• Provide technical Leadership and Guidance: establishing research and coding standards, system design principles, and best practices; guiding the group in overcoming technical challenges while ensuring that our codebase remains efficient and maintainable
• Drive Solution Architecture and Design: designing research programs and software systems that meet the requirements of a diverse stakeholder group; planning implementation in a way
• Perform Hands-On Coding and Code Review
• Lead Collaboration and communication: collaborating closely with other group members and stakeholders including project managers, product owners, and other engineering teams; ensuring that technical options and decisions, and project timelines, are clearly communicated to all stakeholders
• Model Continuous Learning and Skill Development: helping the group and the broader team to stay up-to-date with the latest tools and techniques in both the machine learning and security fields; fostering a culture of continuous learning and skill development; mentoring more junior colleagues

• 10+ years of experience solving complex problems using modern AI/ML techniques
• Complete mastery of a subset of the languages, frameworks and tools currently used in the development of data intensive, machine learning systems, e.g. Spark, Airflow, Python, TensorFlow/PyTorch
• Experience in designing, training, and evaluating models
• Master’s Degree in any quantitative science or engineering field. Additional or directly relevant experience will be considered in lieu of a degree
• Experience with the full system development lifecycle, from requirement gathering, research, design, implementation, testing, through deployment/launch and operation
• Comfort-level working cross-functionally across both research and product teams

Nice to Haves:

• Postgraduate or advanced degree in a relevant research domain
• First-author publications at peer-reviewed AI conferences
• Experience with model parallelism for working with large neural network architectures
• Expertise in natural language processing, especially practical experience developing modern, transformer-based language models
• Experience developing complex applications leveraging API-based LLMs
• Representation learning, especially for temporal sequences
• Open-source foundation models like T5, Bloom, etc.
• Multimodal models and systems
• Lifelong learning systems driven by implicit and explicit product feedback
• Practical reinforcement learning techniques