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
$271,000/year to $347,000/year + bonus + equity + benefits
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