Machine Learning Engineer (Technical Leadership)

Machine Learning Engineer (Technical Leadership) Responsibilities
Drive the team's ML strategy & technical direction to pursue opportunities that advance machine learning capabilities across the organization
Design and develop end-to-end machine learning systems, from data pipelines to model training, evaluation, and deployment
Lead experimentation and A/B testing frameworks to measure and optimize model performance
Build highly scalable classifiers and ML tools leveraging deep learning, data regression, and rules-based models
Adapt and optimize machine learning methods for modern parallel environments (e.g., distributed clusters, multicore SMP, and GPU)
Partner with research teams to translate cutting-edge ML research into production systems
Mentor and influence ML engineers across organizations, raising the bar for ML best practices
Identify new ML opportunities for the larger organization and influence staffing/prioritization of these initiatives
Effectively communicate complex ML systems and architectural decisions to technical and non-technical stakeholders

Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Experience leading projects with industry-wide impact
Experience communicating and working across functions to drive solutions
Experience in mentoring/influencing engineers across organizations
Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term vision
Experience in driving large cross-functional/industry-wide engineering efforts
12+ years of experience in programming languages (Python, C++, or Java) with technical background
8+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or deep learning based methods

Preferred Qualifications
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience with deep learning frameworks (PyTorch, TensorFlow) and ML infrastructure tools
Familiarity with MLOps practices, model monitoring, and production ML systems
Experience building and optimizing large-scale model training pipelines
Experience shipping ML-powered products to millions of users or launching new ML product lines
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Publications or contributions to the ML research community