Research Scientist, ML Systems

Overview

Research Scientist, ML Systems (HPC & Systems Optimization)

New York, NY (Hybrid, 3 days in office)

Highly competitive compensation package

Join an elite technology and research group at the forefront of global finance, where world-class engineering and quantitative research converge to solve some of the most complex problems in any industry. Their teams are composed of passionate, first-principles thinkers who operate in one of the world's most demanding high-performance computing environments. We are seeking a visionary systems specialist to join them and re-engineer the fundamental building blocks of their machine learning models, enabling the next generation of quantitative research.

We are seeking a specialist with a Ph.D. for a unique role that sits at the deep intersection of ML algorithms and high-performance hardware, much in the vein of researchers like Tri Dao. This is not a typical ML position. It s a role for a true systems builder who can optimize the core computational mechanics of complex models through low-level, hardware-aware development.

You will have the autonomy and resources to dive deep into the stack, profile performance bottlenecks, and write highly optimized code to push the boundaries of what’s possible on the latest hardware.

• Re-engineer the fundamental building blocks of complex machine learning models to achieve massive performance gains.
• Design and implement novel numerical algorithms in C++ and CUDA to accelerate model training and inference.
• Profile and analyze deep learning workloads to identify and solve non-obvious performance bottlenecks across the entire system, from the CPU to the GPU and interconnect.
• Collaborate with world-class quantitative researchers and engineers to co-design and implement the next generation of ML systems and infrastructure.
• Stay at the cutting edge of academic and industry research in HPC, computer architecture, and ML systems.

• A Ph.D. in Computer Science, Electrical & Computer Engineering, Physics, or a related technical field with a strong publication record.
• Deep, hands-on expertise in high-performance computing (HPC) and parallel programming models (e.g. CUDA, MPI, OpenMP).
• Expert-level proficiency in C++ for performance-critical development.
• Demonstrable experience in low-level systems optimization, performance profiling, and identifying hardware bottlenecks (e.g. memory bandwidth, latency).
• A background in optimizing numerical algorithms, computational mechanics, or compiler technologies is a significant plus.