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
•
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.
Professional Skills & Competencies
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.