Source description
About the role
Applicable Field of Work
- Machine Learning & AI for Mathematical Discovery and Reasoning — R&D at the intersection of deep learning, neuro-symbolic methods, automated theorem proving, and pure/applied mathematics.
DUTIES & RESPONSIBILITIES
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Lead core discovery projects (e.g., successors to PatternBoost): set research agendas, design and run large-scale experiments to reveal latent mathematical structures, and publish high-impact papers in top AI and mathematics venues.
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Collaborate with research mathematicians to identify open problems to tackle, formulate them into benchmarkable ML objectives, build reproducible pipelines, and iterate toward state-of-the-art solutions.
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Communicate results broadly through peer-reviewed publications, conference talks, open-source releases, and internal briefings that translate research insights into business value.
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Mentor and coach junior researchers by providing technical guidance, rigorous code reviews, and career development support, fostering a culture of excellence and collaboration.
PROFESSIONAL SKILLS & COMPETENCIES
Hard Skills
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Advanced coding in Python and modern ML frameworks (PyTorch, JAX, TensorFlow).
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Deep expertise in large-scale training, reinforcement learning, program synthesis, and neuro-symbolic techniques.
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Strong foundation in higher mathematics (algebra, analysis, combinatorics) and formal proof systems (Lean, Coq, Isabelle).
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Demonstrated research acumen: experiment design, rigorous analysis, and a track record of peer-reviewed publications.
Soft Skills
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Exceptional scientific writing and presentation abilities, tailoring complex ideas to diverse audiences.
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Proven collaborator who thrives in interdisciplinary teams with mathematicians, engineers, and product stakeholders.
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Leadership and mentorship strengths—able to inspire, guide, and elevate a high-performance research culture.