Senior Manager Applied ML For Systems (AI)

TL;DR

Senior Manager Applied ML For Systems (AI): Lead a team of machine learning engineers to develop and deploy ML and optimization algorithms improving hirify.global infrastructure efficiency and performance with an accent on scalable ML solutions, AI model deployment, and infrastructure optimization. Focus on setting vision and strategy, driving high-impact AI initiatives, and managing talent in a high-growth environment.

Location: San Francisco, United States (Onsite)

Salary: $222,000–$300,000 USD

Company

hirify.global is a leading data and AI company providing a unified data intelligence platform used by over 10,000 organizations worldwide, including more than 50% of the Fortune 500.

What you will do

• Set vision and strategy for ML-for-Systems initiatives aligned with product and infrastructure priorities.
• Build and lead a high-performing applied ML engineering team with mentorship and career development.
• Collaborate with engineering and product leaders to identify AI and optimization opportunities in infrastructure.
• Oversee development and deployment of state-of-the-art AI models and optimization systems improving products and services.
• Guide design of robust, scalable ML solutions ensuring best practices in production environments.
• Represent the company in AI community through publications and talks.

Requirements

• Leadership experience managing applied AI/ML teams in fast-paced environments.
• Strong technical foundation in computer systems, statistics, and applied ML.
• Knowledge or interest in mathematical modeling beyond ML (e.g., operations research, combinatorial optimization).
• Experience delivering ML systems at scale in production including infrastructure and processes.
• Interest or experience with compute systems such as Kubernetes and Spark.
• English proficiency at least B2 and ability to collaborate cross-functionally.

Culture & Benefits

• Comprehensive benefits tailored to employee needs.
• Commitment to diversity and inclusion in hiring and culture.
• Opportunities for professional growth and technical excellence.
• Work in a high-impact, customer-obsessed environment.

Hiring process

• Evaluation of leadership and technical expertise.
• Interviews focusing on applied ML and systems knowledge.
• Assessment of cultural fit and collaboration skills.