Member of Technical Staff
Axiom Math - AxiomHQ, Palo Alto, California, United States
Posted Apr 3, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Market context
- Median wage (BLS OEWS)
- $116,543 national median
- Projected growth (BLS Employment Projections)
- +9.8% - Much faster than average
Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Schedule
- Shift type
- Not verified
- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Member of Technical Staff AxiomHQ, Palo Alto, California, United States 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 - 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
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