{"id":3428137,"url":"https://alion.io/job/susquehanna-international-group-machine-learning-engineer-inference-optimization-experienced-hire","title":"Machine Learning Engineer- Inference Optimization | 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":156000,"max_usd":348000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C#","optional":false},{"name":"C++","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rust","optional":false},{"name":"CUDA","optional":true},{"name":"CUTLASS","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Kubernetes","optional":true},{"name":"Model Distillation","optional":true},{"name":"Quantization","optional":true},{"name":"ROCm","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-10-28T23:14:43Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-04T05:04:56Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Overview\nWe are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at the intersection of machine learning, systems engineering, and GPU performance. You will work on inference workloads where latency, throughput, reliability, and hardware efficiency all matter, and where a deep understanding of modern inference runtimes can meaningfully improve production outcomes.\nYou will work closely with quantitative researchers and engineers to understand model structure, identify inference bottlenecks, and turn research ideas into efficient production systems. The work may involve other types of models, but focuses on transformer-style architectures, and structured inference workloads. You will evaluate and tune frameworks and related serving or compilation systems, while also reasoning about GPU execution, memory layout, batching strategies, precision tradeoffs, and end-to-end latency.\nWhat you'll do\nDesign, build, and optimize low-latency inference systems for production machine learning workloads.\nProfile model inference pipelines across model execution, runtime configuration, batching, memory movement, serialization, networking, and I/O.\nEvaluate, integrate, and tune inference runtime systems.\nImprove latency, throughput, GPU utilization, for production inference workloads.\nBuild and support benchmarking and profiling tools to compare model variants, hardware targets, runtime configurations, and deployment strategies.\nDebug performance issues involving GPU memory, compute saturation, kernel behavior, CPU/GPU coordination, data movement, and serving-layer overhead.\nHelp shape model and system design choices so that research models are efficient to deploy under real latency constraints.\nWhere necessary, collaborate with lower-level systems or GPU specialists on custom operators, kernel-level optimization, or hardware-specific performance work.\nWhat we’re looking for\nExperience deploying, optimizing, or operating machine learning inference workloads in production or production-like environments.\nProgramming experience in Python, Java, C# etc. and at least one systems language such as C, C++, Rust, or Go\nSolid understanding of modern ML frameworks such as PyTorch, including model execution, export, tracing, compilation, and performance profiling.\nAbility to reason about latency, throughput, batching, memory use, GPU utilization, and reliability under real workloads.\nStrong practical judgment around tradeoffs between model quality, latency, throughput, implementation complexity, and maintainability.\nPreferred qualifications\nExperience optimizing inference for latency-sensitive or high-throughput applications.\nExperience with model optimization techniques such as quantization, pruning, distillation, operator fusion, graph lowering, custom operators, or model compilation.\nExposure to CUDA, Triton language, ROCm, PTX, CuTe, CUTLASS, FlashInfer, or similar low-level GPU programming tools.\nExperience running inference workloads on Kubernetes or GPU clusters, including scheduling, autoscaling, observability, and resource management.\nBackground in mathematics, physics, computer science, engineering, statistics, quantitative finance, or another technical field.\nDemonstrated ability to improve real-world inference performance beyond a baseline framework implementation.\nIf you're a recruiting agency and want to partner with us, please reach out to . 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