Position Summary
Two Sigma is building a new team to drive the firm's strategic transition from CPU-centric to GPU-accelerated computation. Accelerated Compute sits within AI Innovation and operates at the intersection of quantitative modeling workflows, GPU performance engineering, and infrastructure strategy.
You are a GPU programming expert. You write CUDA, you optimize kernels, you understand the memory hierarchy, you know why naive GPU code is slow and how to make it fast. You will ensure that when workloads move to GPU, they achieve the performance that justifies the transition.
You will take on the following responsibilities:- Design and implement GPU-accelerated kernels for financial computation workloads
- Optimize GPU code for throughput, latency, and memory efficiency across current and next-generation hardware (Blackwell, Rubin)
- Develop procedures for precision management (FP8/FP4 training and inference) in financial applications
- Profile and optimize GPU workloads using NVIDIA tooling (Nsight Systems, Nsight Compute)
- Build reusable GPU libraries and abstractions that modeling teams can use without requiring deep CUDA expertise
- Evaluate and integrate GPU-accelerated libraries (RAPIDS, CUTLASS, cuBLAS, TensorRT) for financial use cases
You should possess the following qualifications:- BS or MS in Science, Technology, Engineering or Math
- Minimum 1 year of experience required; 4-10 years of experience preferred
- Expert-level CUDA programming: kernel development, memory management, stream and graph optimization
- Deep understanding of GPU architecture: SM structure, warp scheduling, memory hierarchy (registers, shared memory, L1/L2, HBM)
- Experience with performance profiling and optimization of GPU workloads
- Strong C++ and Python skills, as well as familiarity with mixed-precision computation and numerical stability
- Track record of delivering meaningful speedups on real workloads (not just benchmarks)
Preferred experience:- Background in HPC, scientific computing, or computational finance
- Experience with multi-GPU and multi-node GPU programming (NCCL, MPI)
- Familiarity with GPU-accelerated data processing frameworks (RAPIDS, cuDF)
You will enjoy the following benefits:- Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
- Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
- Learning: Tuition reimbursement, conference and training sponsorship
- Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
- Hybrid Work Policy: Flexible in-office days with budget for home office setup
The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
