Math for AI Jobs

Research internship at Graphcore focused on advancing AI/ML through hardware-aware algorithms and efficient training/inference methods. Candidates will conduct self-directed research projects across diverse areas including model optimization, generative AI, and scientific applications, with mentorship and opportunities to publish at leading conferences.

This internship offers the opportunity to develop high-performance ML compute kernels for next-generation AI hardware at Graphcore, focusing on optimizing tensor operations and linear algebra computations. You'll work with C++ to implement and optimize kernels for CNNs and LLMs while profiling performance across threading, cache locality, and memory efficiency. This role bridges machine learning and systems engineering, requiring strong fundamentals in numerical computing and hardware-aware optimization techniques.

Graduate-level software engineering role focused on designing and optimizing high-performance ML compute kernels for AI hardware accelerators. The position involves implementing tensor operations, linear algebra routines, and fused operations in C++ while applying hardware-aware optimization techniques for maximum performance on next-generation AI processors.

Senior ML Engineer role focused on Operations Research at Instacart's Logistics team, solving complex fulfillment problems including order batching, shopper routing, and real-time assignment. The position combines combinatorial optimization and mathematical programming with ML to optimize a multi-sided marketplace for customers, shoppers, and retailers at scale.

Lead the algorithmic design of Instacart's real-time bidding and budget optimization systems that handle millions of daily decisions for their $1B+ ads business. Apply control theory, constrained optimization, and auction economics to balance advertiser goals, user experience, and platform revenue. Own systems end-to-end from mathematical formulation through production deployment and impact measurement.

This role focuses on building macroeconomic models to forecast AI's transformative impact on growth, labor markets, and income distribution. You'll develop scenario-based forecasting tools grounded in real-world AI usage data from the Anthropic Economic Index and employ cutting-edge growth theory and computational methods to reason about unprecedented economic trajectories.

Anthropic is seeking research scientists to work on mechanistic interpretability—understanding how neural networks function at a fundamental level by reverse-engineering their parameters into meaningful algorithms. The role focuses on discovering and documenting the underlying computational mechanisms of large language models to enable safer, more trustworthy AI systems. This is a research-focused position requiring strong theoretical foundations and hands-on experimentation with neural network analysis tools.

This role focuses on measuring and understanding AI's economic impact through rigorous empirical research and novel methodologies. The economist will develop the Anthropic Economic Index using frontier econometric techniques, machine learning, and privacy-preserving data systems to analyze AI's effects on labor markets, productivity, and economic transformation.

This is an expression of interest for a Research Manager role on Anthropic's Interpretability team, focused on mechanistically understanding how neural networks work as a core AI safety initiative. The role emphasizes reverse-engineering trained models, discovering how neural network parameters map to meaningful algorithms, and building scientific foundations for neural network safety. Currently, the team is actively hiring Research Engineers and Research Scientists for individual contributor roles in this area.