Software Engineer (Technical Leadership) - Machine Learning Responsibilities:

• Drive the team's goals and technical direction to pursue opportunities that make your larger organization more efficient
• Effectively communicate complex features and systems in detail
• Understand industry & company-wide trends to help assess & develop new technologies
• Partner & collaborate with organization leaders to help improve the level of performance of the team & organization
• Identify new opportunities for the larger organization & influence the appropriate people for staffing/prioritizing these new ideas
• Lead long term technical vision and roadmap for large cross-company efforts
• Suggest, collect and synthesize requirements and create an effective feature roadmap
• Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt

Minimum Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term mission
• Experience driving large cross-functional/industry-wide engineering efforts
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
• Experience communicating and working across functions to drive solutions
• Experience mentoring/influencing executive stakeholders across organizations
• 12+ years of programming experience in a relevant language OR 8+ years experience with a PhD

Preferred Qualifications:

• Demonstrated experience with data structures and algorithms, including graph theory and optimization techniques
• Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn
• Understanding of information retrieval concepts, such as indexing, querying, and ranking
• Masters degree or PhD in Computer Science or a related technical field
• Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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