GPU Performance Engineer

Position SummaryTwo 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.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 ResponsibilitiesDesign and implement GPU-accelerated kernels for financial computation workloadsOptimize 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 applicationsProfile 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 expertiseEvaluate and integrate GPU-accelerated libraries (RAPIDS, CUTLASS, cuBLAS, TensorRT) for financial use casesYou Should Possess The Following QualificationsBS or MS in Science, Technology, Engineering or MathMinimum 1 year of experience required; 4-10 years of experience preferred Expert-level CUDA programming: kernel development, memory management, stream and graph optimizationDeep understanding of GPU architecture: SM structure, warp scheduling, memory hierarchy (registers, shared memory, L1/L2, HBM)Experience with performance profiling and optimization of GPU workloadsStrong C++ and Python skills, as well as familiarity with mixed-precision computation and numerical stabilityTrack record of delivering meaningful speedups on real workloads (not just benchmarks)Preferred ExperienceBackground in HPC, scientific computing, or computational financeExperience with multi-GPU and multi-node GPU programming (NCCL, MPI)Familiarity with GPU-accelerated data processing frameworks (RAPIDS, cuDF)You Will Enjoy The Following BenefitsCore Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurancePerks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game roomsLearning: Tuition reimbursement, conference and training sponsorshipTime Off: Generous vacation and unlimited sick days, competitive paid caregiver leavesHybrid Work Policy: Flexible in-office days with budget for home office setupThe 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.We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.Two Sigma is committed to providing reasonable accommodations to qualified individuals in accordance with applicable federal, state, and local laws.If you believe you need an accommodation, please visit our website for additional information.