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Director of Machine Learning – 1811

San Francisco, CA

Director of Machine Learning – 1811

Location: Remote (United States)
Employment Type: Direct Hire - Full-Time
Compensation: $180K-$250K - based on experience + equity
Residency Requirements: US Citizens and all other parties authorized to work in the US are encouraged to apply.


About the Opportunity

A rapidly growing technology company is seeking an experienced Machine Learning leader to build and scale intelligent decision-making systems that power critical business functions. This role combines technical leadership, people management, and strategic product ownership, making it ideal for someone who enjoys developing production-grade machine learning solutions while leading high-performing engineering and data science teams.

You'll work closely with executive leadership and cross-functional partners to define machine learning strategy, oversee model development from concept through deployment, and expand a portfolio of AI-driven products that solve complex, high-impact business problems.

What You'll Do

  • Lead and mentor a team of machine learning engineers and data scientists.
  • Own the roadmap for multiple machine learning initiatives that directly support core business operations.
  • Guide the full machine learning lifecycle, including data preparation, feature engineering, model development, deployment, monitoring, and continuous optimization.
  • Partner with engineering, product, and executive leadership to prioritize initiatives and deliver measurable business impact.
  • Establish best practices for scalable model development, experimentation, and production deployment.
  • Drive technical excellence while maintaining a hands-on understanding of implementation and architecture.
  • Communicate technical concepts and business outcomes clearly to stakeholders across the organization.
  • Help recruit, develop, and retain top machine learning talent as the organization continues to grow.

Required Qualifications
  • 7+ years of experience building and deploying production machine learning systems.
  • 4+ years leading machine learning or data science teams in fast-paced technology organizations.
  • Proven success delivering multiple production ML solutions that solve meaningful business challenges.
  • Strong programming skills in Python and experience developing production-quality software.
  • Deep understanding of feature engineering, model training, deployment, monitoring, and performance optimization.
  • Experience partnering with engineering teams to deliver scalable, reliable machine learning platforms.
  • Excellent communication skills with the ability to influence technical and executive stakeholders.
  • Comfortable participating in technical interviews, including live coding discussions.

Preferred Background
  • Successful candidates often bring experience from industries where machine learning plays a critical role in risk assessment, trust, security, financial systems, identity, healthcare technology, or other high-reliability environments.
  • Experience building predictive models for operational decision-making, anomaly detection, classification, or risk evaluation is highly valued.

Education
  • Master's or PhD in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative discipline preferred.
  • Exceptional candidates with a Bachelor's degree and outstanding industry experience are encouraged to apply.

What We're Looking For
  • Demonstrated ownership of machine learning products from concept through production.
  • Experience scaling both teams and technical platforms in high-growth environments.
  • Strong balance of strategic leadership and technical depth.
  • Ability to make data-driven decisions while aligning technical work with business objectives.
  • History of mentoring high-performing technical teams.

Nice to Have
  • Experience in highly regulated or risk-sensitive industries.
  • Familiarity with modern MLOps practices and production monitoring.
  • Background working in startup or growth-stage environments where adaptability and ownership are essential.