Undisclosed

HPC Research Engineer

LocationNew York, United States
Job Typefull_time

About This Job

We are looking for a Research Engineer

with a strong background in

High-Performance Computing (HPC)

to join our research team.

This role blends deep systems engineering and quantitative research, with a focus on designing ultra-efficient infrastructure and tooling to accelerate the development and deployment of machine learning models in

systematic trading

.

You will work side-by-side with researchers and modelers to push the boundaries of ML scale and performance—from novel numerical algorithms to compiler-level optimization and distributed GPU training. The systems you build will underpin the next generation of our firm’s ML research and production strategy.


Role Responsibilities

Architect and scale distributed ML training platforms to support increasingly large, complex models while maintaining high throughput and fast iteration cycles.

Develop high-performance numerical computing pipelines optimized for modern GPU and multi-node systems.
Collaborate with researchers to rapidly prototype and productionize novel model architectures, including transformers, tree-based models, or hybrid approaches.
Profile, benchmark, and optimize system performance across the stack: GPU kernels, interconnect (NVLink/InfiniBand), I/O, and compiler stacks (e.g., TorchInductor, XLA, Triton).
Implement reproducible, testable research infrastructure with best practices for traceability, versioning, and benchmarking.
Contribute to the design of job schedulers, experiment tracking tools, and data loading pipelines tuned for heterogeneous compute clusters.


Technical Requirements

Expert-level programming skills in

Python

and strong proficiency with

numerical/ML frameworks

(PyTorch, JAX, NumPy). Familiarity with

PyTorch 2.x compiler stack

(TorchScript, TorchInductor) and MLIR/XLA/Triton is a strong plus.

Solid experience in

distributed systems

,

parallel computing

, and

performance tuning

(e.g., CUDA, NCCL, MPI, OpenMP, or similar).

Hands-on understanding of

GPU architecture

, memory hierarchies, SMs, tensor cores, and profiling tools like nvprof, Nsight, or perf.

Experience deploying or maintaining large-scale model training systems, including

multi-node orchestration

and

experiment reproducibility

.

Bachelor's, Master’s, or PhD in Computer Science, Applied Mathematics, Engineering, or a related field with an emphasis on numerical computing or systems.

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