We are looking for a Software Engineer specializing in ML Systems Engineering to join Development department.
While vision and language models have become increasingly commoditized, proprietary deep learning models are unique, fast-evolving, and deployed in live trading across the worlds most efficient and sophisticated financial markets. Operating in this environment presents distinct scaling challenges and continuous opportunities for optimization. Success in this role requires first-principles thinking and a deep understanding of the engineering trade-offs behind high-performance DL systems.
This is a pivotal role within engineering organization. You will work closely with researchers and engineers across the company, running deep learning models on massive compute clusters and adapting them for production serving under strict and non-trivial constraints.
While vision and language models have become increasingly commoditized, proprietary deep learning models are unique, fast-evolving, and deployed in live trading across the worlds most efficient and sophisticated financial markets. Operating in this environment presents distinct scaling challenges and continuous opportunities for optimization. Success in this role requires first-principles thinking and a deep understanding of the engineering trade-offs behind high-performance DL systems.
This is a pivotal role within engineering organization. You will work closely with researchers and engineers across the company, running deep learning models on massive compute clusters and adapting them for production serving under strict and non-trivial constraints.
Requirements:
B.Sc. with honors in CS/EE/Math/Physics, or a related field from a top-tier university
5+ years of hands-on experience building and deploying large-scale deep learning systems in production
Preferred Qualifications :
M.Sc. or Ph.D. in a relevant quantitative field – Advantage
Proficiency in Python/C/C++
Deep, working knowledge of PyTorch internals
Strong experience in several of the following areas:
Performance profiling and optimization of deep learning workloads
Orchestrating and optimizing large-scale distributed training (hundreds to thousands of GPUs)
Optimizing model serving and inference pipelines (quantization, distillation, compilation, memory optimization, etc.)
Training and scaling state-of-the-art vision, language, or diffusion models
Implementing custom CUDA/Triton kernels
B.Sc. with honors in CS/EE/Math/Physics, or a related field from a top-tier university
5+ years of hands-on experience building and deploying large-scale deep learning systems in production
Preferred Qualifications :
M.Sc. or Ph.D. in a relevant quantitative field – Advantage
Proficiency in Python/C/C++
Deep, working knowledge of PyTorch internals
Strong experience in several of the following areas:
Performance profiling and optimization of deep learning workloads
Orchestrating and optimizing large-scale distributed training (hundreds to thousands of GPUs)
Optimizing model serving and inference pipelines (quantization, distillation, compilation, memory optimization, etc.)
Training and scaling state-of-the-art vision, language, or diffusion models
Implementing custom CUDA/Triton kernels
This position is open to all candidates.
