Intern R&D Scientist / Principal AI Researcher_Excellon Software_Pune

Interested candidates can share their updated resume on [email removed], [email removed]

Job Title: Intern - R&D Scientist / Principal AI Researcher

(Neuro-Symbolic & Foundational AGI)

Job Location: Pune (work from office only)

Experience: Engineering Freshers or Mid-to-Senior (Ph.D. / Advanced Post-Graduate preferred

Department: Core Research & Development (R&D) - Artificial Learning, Machine Learning & Data Science.

Project Internship Duration: 3 to 5 months

JOB DESCRIPTION:

• Core R&D Architecture: Design, simulate, and implement hybrid Neuro-Symbolic frameworks that integrate deep learning with formal logic, knowledge graphs, and automated reasoning.
• Mathematical Modeling: Apply advanced mathematical concepts to solve fundamental AGI bottlenecks, particularly regarding abstraction, compositionality, and sample efficiency.
• Algorithmic: Develop custom optimization techniques for complex, non-linear, and high-dimensional manifolds.
• Academic Translation: Convert cutting-edge theoretical research from top-tier AI conferences (NeurIPS, ICML, ICLR, IJCAI) into robust, scalable scientific code.
• Peer Leadership: Collaborating with senior architects to map out the long-term foundational AI roadmap for Excellon's product ecosystem.

REQUIRED TECHNICAL FOUNDATIONS:

• Because of the heavy theoretical demands of this role, candidates must possess a rigorous grasp of the following domains:
• Advanced Mathematics & Statistics: Deep knowledge of probabilistic graphical models, Bayesian inference, stochastic processes, and statistical mechanics.
• Information Geometry: Practical or theoretical understanding of statistical manifolds, Riemannian geometry applied to probability distributions, and Fisher information metrics.
• Non-linear Optimization: Expertise in non-convex optimization, gradient flows on manifolds, constrained optimization, and evolutionary/symbolic optimization methods.
• Formal Logic & Symbolic AI: Familiarity with first-order logic, description logic, knowledge representation, automated theorem proving, or semantic web technologies

QUALIFICATIONS / TECH SKILLS:

• Education: B.Tech, Ph.D., M. Tech, or M.Sc. in Computer Science, Applied Mathematics, Statistics, Physics, Data Science, or highly quantitative Scientific Computing tracks.
• Experience: Proven track record in mathematical modeling, core algorithm design, or deep tech R&D. Peer-reviewed publications in top-tier journals/conferences are a massive plus.
• Programming Literacy: Exceptional proficiency in Python, C++, Julia, or R, alongside deep scientific computing stacks (e.g., JAX, PyTorch, NumPy, SciPy) optimized for custom gradient tracking and mathematical simulation.