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.
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.
distributed systems
,
parallel computing
, and
performance tuning
(e.g., CUDA, NCCL, MPI, OpenMP, or similar).
GPU architecture
, memory hierarchies, SMs, tensor cores, and profiling tools like nvprof, Nsight, or perf.
multi-node orchestration
and
experiment reproducibility
.
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