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.
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.
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.
Communicate results broadly through peer-reviewed publications, conference talks, open-source releases, and internal briefings that translate research insights into business value.
Mentor and coach junior researchers by providing technical guidance, rigorous code reviews, and career development support, fostering a culture of excellence and collaboration.
Hard Skills
Advanced coding in Python and modern ML frameworks (PyTorch, JAX, TensorFlow).
Deep expertise in large-scale training, reinforcement learning, program synthesis, and neuro-symbolic techniques.
Strong foundation in higher mathematics (algebra, analysis, combinatorics) and formal proof systems (Lean, Coq, Isabelle).
Demonstrated research acumen: experiment design, rigorous analysis, and a track record of peer-reviewed publications.
Soft Skills
Exceptional scientific writing and presentation abilities, tailoring complex ideas to diverse audiences.
Proven collaborator who thrives in interdisciplinary teams with mathematicians, engineers, and product stakeholders.
Leadership and mentorship strengths—able to inspire, guide, and elevate a high-performance research culture.