Principal AI/ML Talent Sourcing Strategist

Principal AI/ML Talent Sourcing Strategist

Role Overview

We are seeking a Principal-level AI/ML Talent Sourcing Strategist with deep expertise in identifying and engaging exceptional talent across Artificial Intelligence, Machine Learning, Generative AI, and advanced engineering ecosystems.

This individual will lead strategic talent identification for highly specialized and competitive AI/ML roles, leveraging advanced sourcing methodologies, technical intelligence, talent mapping, and industry networks.

The ideal candidate combines strong technical understanding with sophisticated sourcing capabilities to identify top researchers, engineers, applied scientists, and AI leaders across global talent markets.

Key Responsibilities

• Strategic AI/ML Talent Sourcing
• Lead sourcing strategies for Principal, Staff, Senior and Distinguished-level AI/ML professionals.
• Identify exceptional talent across Generative AI, LLMs, NLP, Computer Vision, Deep Learning, Reinforcement Learning and Applied AI.
• Build talent pipelines for AI Research Scientists, ML Engineers, Applied Scientists, AI Architects and Research Engineers.
• Develop targeted sourcing strategies for niche and highly competitive AI talent pools.
• Advanced Technical Research & Talent Intelligence
• Leverage GitHub, Hugging Face, arXiv, Google Scholar, LinkedIn, Kaggle, Papers With Code and research communities to identify high-caliber talent.
• Identify influential researchers through publications, citations, patents, open-source contributions and conference participation.
• Conduct competitor talent mapping across leading AI labs, technology companies, research institutions and startups.
• Track emerging AI technologies, research breakthroughs and talent movements.
• Technical Talent Assessment
• Develop the ability to interpret complex AI/ML job requirements and translate them into precise sourcing strategies.
• Understand technical distinctions across Machine Learning, Deep Learning, LLMs, RAG, Agentic AI, Model Training, Fine-Tuning, MLOps and AI Infrastructure.
• Evaluate candidate profiles based on technical depth, research impact, engineering contributions and practical implementation experience.
• Distinguish between academic research profiles, applied research professionals and production-focused AI/ML engineers.
• Executive & Passive Talent Engagement
• Build relationships with highly sought-after AI/ML professionals, including passive candidates and established researchers.
• Develop personalized outreach strategies for senior technical talent.
• Engage candidates through research communities, technical conferences, AI meetups and professional networks.
• Establish long-term relationships with AI/ML experts and emerging technical leaders.
• Stakeholder Partnership
• Partner with AI/ML hiring managers, Principal Engineers, Research Scientists and technical leadership.
• Translate workforce requirements into actionable talent intelligence and sourcing plans.
• Advise hiring teams on talent availability, market compensation, competitor landscapes and sourcing challenges.
• Present talent market insights and strategic hiring recommendations to leadership.
• Talent Pipeline & Market Mapping
• Build and maintain comprehensive talent maps across AI/ML domains.
• Develop competitor intelligence and talent availability reports.
• Establish proactive pipelines for future AI/ML hiring requirements.
• Track sourcing effectiveness, engagement conversion, candidate quality and pipeline health.

Required Qualifications

• 8–12+ years of progressive technical sourcing or talent acquisition experience, preferably with significant AI/ML hiring exposure.
• Demonstrated experience sourcing Principal, Staff, Senior and Distinguished-level engineering or research talent.
• Strong understanding of AI/ML technologies and research ecosystems.
• Hands-on experience sourcing AI/ML professionals across Generative AI, LLMs, NLP, Computer Vision, Deep Learning and Reinforcement Learning.
• Advanced Boolean search, X-ray search and market-mapping capabilities.
• Experience using GitHub, Hugging Face, arXiv, Google Scholar, LinkedIn and other specialized technical platforms.
• Ability to evaluate research publications, open-source contributions, patents and technical portfolios.
• Exceptional stakeholder management, candidate engagement and executive communication skills.
• Proven ability to independently lead complex, niche and strategic talent searches.

Preferred Qualifications

• Experience recruiting for leading AI research labs, hyperscalers, semiconductor companies or advanced technology organizations.
• Exposure to AI research conferences such as NeurIPS, ICML, ICLR, CVPR and ACL.
• Experience building talent intelligence programs and strategic sourcing functions.
• Familiarity with AI infrastructure, distributed training, GPU computing and model deployment ecosystems.
• Experience hiring PhD-level researchers and professionals from leading research institutions.

Core Technical Domains

• Generative AI & Large Language Models (LLMs)
• Agentic AI & Autonomous AI Systems
• Machine Learning & Deep Learning
• Natural Language Processing (NLP)
• Computer Vision & Multimodal AI
• Reinforcement Learning
• AI Research & Applied Science
• MLOps & AI Infrastructure
• Foundation Models & Model Training
• Retrieval-Augmented Generation (RAG) and Fine-Tuning

Success Metrics

• Quality and depth of strategic AI/ML talent pipelines.
• Successful identification of niche and senior-level AI/ML professionals.
• Passive candidate engagement and response rates.
• Qualified candidate conversion into technical interviews.
• Time-to-identify for critical AI/ML talent.
• Hiring manager satisfaction and sourcing intelligence quality.
• Development of long-term AI/ML talent communities.

EEO

“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”