{"id":1602650,"url":"https://alion.io/job/susquehanna-international-group-gpu-performance-engineer-experienced-hire","title":"GPU Performance Engineer | Experienced Hire","company":{"id":4917,"name":"Susquehanna International Group","domain":"sig.com","url":"https://alion.io/company/sig","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Philadelphia, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":163000,"max_usd":359000,"period":"year","method":"role_country_seniority_unknown","sample_n":4194},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"Apache TVM","optional":true},{"name":"LightGBM","optional":true},{"name":"Machine Learning","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-10-01T19:35:46Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-22T13:06:54Z","board_verified":true,"closed_at":null,"days_open":20,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":20},"description":"Overview\nWe are looking for a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. This role is focused on workloads where off-the-shelf runtimes and vendor libraries do not fully exploit the structure of the model, and where custom kernels, memory layouts, and execution strategies can deliver meaningful gains.\nYou will work closely with quantitative researchers and engineers to understand model structure, identify computational bottlenecks, and turn mathematical ideas into production-grade GPU implementations. You will use your understanding of GPU hardware to help shape models that are both mathematically effective and efficient to run. The problems span compact neural networks, tree-based models, and other structured inference workloads where latency, throughput, and efficiency all matter.\nThis role is a strong fit for someone who enjoys low-level optimization, performance analysis, and translating abstract models into hardware-efficient code.\nWhat you'll do\nDesign, implement, and optimize custom CUDA kernels for latency-critical inference workloads \nDevelop fine-grained GPU implementations tailored to specific model structures\nAnalyze quantitative research models and computational bottlenecks to identify opportunities for parallelization and hardware-efficient execution \nCollaborate directly with quantitative researchers to translate mathematical models into high-performance compute pipelines \nOptimize end-to-end inference performance through kernel tuning, memory-layout design, execution strategy, I/O optimization, and precision tradeoffs \nProfile and benchmark GPU performance \nImprove latency and throughput in production inference systems \nContribute to GPU architecture decisions and performance best practices \nWhat we’re looking for\nStrong proficiency in writing and optimizing CUDA kernels \nSolid programming experience in C/C++ (preferred) \nDeep understanding of GPU architecture, including memory hierarchy, SIMT execution, occupancy, and latency/throughput tradeoffs\nAbility to reason about numerical stability, precision, performance tradeoffs, and how model design choices affect hardware efficiency\nStrong problem-solving skills and comfort working with low-level systems\nPreferred qualifications\nPhD in mathematics, physics, computer science, engineering, or related quantitative field \nStrong background in linear algebra, probability, numerical methods, or scientific computing\nExperience working with quantitative research teams or financial models\nDemonstrated ability to improve real-world inference performance beyond baseline framework or library implementations\nFamiliarity with PTX-level behavior, tensor core utilization, or architecture-specific tuning \nExposure to ONNX Runtime, TensorRT, Triton, TVM, or similar systems \nExposure to neural networks, tree-based models (e.g., LightGBM), state space models (e.g., Mamba architectures), and experience with kernel fusion, custom operators, model compilation, or graph-level optimization\nAbout Susquehanna\nSusquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.\nIf you're a recruiting agency and want to partner with us, please reach out to . 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