Research Engineer Pre-Training
Jump Trading is a global trading firm where traders, engineers, and researchers develop trading strategies, models, infrastructure, and systems across asset classes and time horizons.
About Jump Trading
Jump Trading is a global trading firm focused on research-driven trading and the engineering of scalable models, tools, infrastructure, and execution systems. Its operations combine trading, technology, AI/ML, and quantitative research, and it also runs research and talent programs including conference travel grants and a fellowship program.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You develop large-scale foundation models for financial market prediction. You own the training stack by building fault-tolerant infrastructure across GPUs and TPUs, engineering high-throughput data pipelines, designing custom kernels, co-designing model architectures, and advancing mixed-precision training and model parallelism.
Requirements
- Significant measurable performance improvements in large-scale distributed training
- Published research in efficient training methods, scaling laws, architectures, or ML systems
- Background in numerical computing, HPC, or distributed systems
- Familiarity with GPUs, TPUs, NVLink, InfiniBand, Kubernetes, Slurm, and operating system internals
- Python
- Deep learning frameworks such as PyTorch or JAX
- Advanced degree in computer science, machine learning, physics, mathematics, or related field, or equivalent frontier-lab experience
- CUDA kernel development preferred
- Triton, Pallas, CuTe, PyTorch internals, JAX internals, XLA optimization, or hardware acceleration preferred
- Reinforcement learning, post-training, or fine-tuning knowledge preferred
- Financial markets or trading knowledge preferred
Responsibilities
- Build fault-tolerant training infrastructure across GPUs and TPUs
- Engineer high-throughput data pipelines
- Design custom kernels
- Co-design model architectures with researchers
- Develop mixed-precision training approaches
- Develop model-parallel training approaches
- Improve pre-training efficiency and performance
Benefits
- Discretionary bonus eligibility
- Medical, dental, and vision insurance
- HSA, FSA, and Dependent Care options
- Employer-paid group term life and AD&D insurance
- Voluntary life and AD&D insurance
- Paid vacation and paid holidays
- Retirement plan with employer match
- Paid parental leave
- Wellness programs