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Symbolica AI

Talent Sourcer – AI & ML Research

Posted 14 Days Ago
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In-Office
London, Greater London, England
Mid level
Easy Apply
In-Office
London, Greater London, England
Mid level
Lead the sourcing strategy for specialized AI talent at Symbolica, focusing on building talent pipelines and utilizing advanced sourcing techniques in a fast-paced environment.
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About Us
Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines.
We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge technologies, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought, not just patterns in data.

Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application.

Founded in 2022, we’ve raised over $30M from leading Silicon Valley investors, including Khosla Ventures, General Catalyst, Abstract Ventures, and Day One Ventures, to push the boundaries of applying formal mathematics and logic to machine learning.

Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight. Join us to redefine the future of AI by turning groundbreaking ideas into reality.

About the role
You'll join the founding team as the first Talent Sourcer and take full ownership of the search for deeply specialised technical talent in the AI field – from ML Engineers to Applied Researchers. This is a rare opportunity to directly shape the direction of the company by helping us hire the people who will build our core technology and the future of Symbolica. You’ll partner with Talent Lead to design and execute searches that go far beyond traditional sourcing playbooks. We’re looking for someone who lives and breathes sourcing, thrives in ambiguity and automation, and brings both creativity and precision to the search for world-class talent.

Your focus

  • Own end-to-end sourcing strategy for highly specialised AI and technical roles
  • Build and nurture long-term pipelines of world-class researchers, engineers, and hybrid profiles
  • Identify and engage talent across unconventional channels and niche talent communities (from conferences to local meet-ups)
  • Design and implement sourcing ops, tools, AI automation, and processes from the ground up in a zero-to-one environment
  • Use advanced sourcing techniques and AI tooling to surface hard-to-find candidates
  • Partner closely with hiring managers to deeply understand role requirements and ideal profiles
  • Ensure a flawless, high-touch candidate experience and communication

About you

  • 3+ years of talent sourcing experience, including at least 1 year focused on sourcing for AI/ML and other highly technical roles in deep tech domains
  • Experience working in fast-paced, ambiguous, early-stage environments
  • Proven success hiring from non-traditional and creative sourcing channels
  • Deep understanding of sourcing tools, AI-powered recruitment platforms, and automation
  • Excellent written communication and ability to pitch a compelling narrative to top talent
  • Naturally curious, resourceful, and relentless in the pursuit of great people

We offer competitive compensation, including an early-stage equity package, with salary and equity levels aligned to your experience and expertise.

Symbolica is an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, gender, age, religion, disability, or sexual orientation.

Top Skills

AI
Ai-Powered Recruitment Platforms
Automation
Ml
Sourcing Tools

Symbolica AI London, England Office

London, United Kingdom

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