Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our team is a group of quantitative researchers, engineers, and ML experts leading foundation model research and trading at Jump. Our mission is to combine emerging techniques and original research to generate signals from financial market data and monetize it globally. We are building the future of ML-powered trading through breakthrough foundation models, and we're looking for an exceptional Pre-Training Engineer to join our team.

Other duties as assigned or needed.

What You'll Do:

As a Pre-Training Research Engineer, you'll be at the forefront of developing massive-scale foundation models that fundamentally transform how we understand and predict markets, where milliseconds matter and no playbook exists. You'll own and drive the entire training stack: building fault-tolerant infrastructure that scales across thousands of GPUs and TPUs with near-linear performance, engineering data pipelines that stream terabytes per second as our models train on petabytes of data from every corner of the global markets, and designing custom kernels that unlock 10x efficiency gains. Co-designing novel architectures with researchers and pioneering cutting-edge approaches to mixed-precision training and model parallelism, you'll have the latest generation hardware at your disposal. This isn't incremental optimization; we're pushing the boundaries of what's possible in pre-training at scale, where your improvements directly impact live trading

Skills You'll Need:

• Expertise and track record of significant, measurable performance improvements in large-scale distributed training (MFU, throughput, convergence, cost-per-token).
• Published research in efficient training methods, scaling laws, architectures, or systems for ML
• Background in numerical computing, HPC, or distributed systems, including familiarity with GPUs/TPUs, high-performance networking (NVLink/InfiniBand), Kubernetes/Slurm, and OS internals
• Expertise in Python and deep experience with modern deep learning frameworks (PyTorch and/or JAX)
• Advanced degree (MS or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field, or equivalent industry experience at a frontier lab
• Ability to balance ambitious research goals with practical engineering constraints
• Strong problem-solving skills, results orientation, and excellent collaborative communication
• Reliable and predictable availability

Bonus Points:

• Expertise in: CUDA kernel development, Triton/Pallas/CuTe DSLs, PyTorch/JAX internals, XLA optimization, or hardware acceleration (FPGA/ASIC)
• Knowledge of reinforcement learning, post-training, or fine-tuning techniques
• Knowledge of financial markets or trading