GPU Performance Engineer

Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors. Our team of engineers, quantitative researchers, and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world’s most complex economic problems.

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.

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

Requirements

• 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)

Benefits

• Fully paid medical and dental insurance premiums for employees and dependents
• Competitive 401k match
• Employer-paid life & disability insurance
• Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
• Tuition reimbursement, conference and training sponsorship
• Generous vacation and unlimited sick days
• Competitive paid caregiver leaves
• Flexible in-office days with budget for home office setup

Pay

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 skills, qualifications, and experience.