{"id":48904,"url":"https://alion.io/job/elastix-ai-compiler-and-performance-engineer","title":"AI Compiler and Performance Engineer","company":{"id":10106,"name":"Elastix","domain":"elastix.ai","url":"https://alion.io/company/elastix","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":6,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":145000,"max_usd":317000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Apache TVM","optional":false},{"name":"C++","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"LLVM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLIR","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TPU","optional":false},{"name":"Triton","optional":false},{"name":"XLA","optional":false}],"status":"live","first_seen_at":"2026-02-24T21:47:48Z","employer_posted_date":"2026-02-24","last_verified_at":"2026-10-17T14:05:52Z","board_verified":true,"closed_at":null,"days_open":234,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":234},"description":"Description\nJob Title: Software Engineer, AI Inference Platform\nCompany: ElastixAI, Inc.\nLocation: Seattle, WA (Hybrid - 3 days/week in office)\nAbout ElastixAI\nElastixAI is an early-stage startup on a mission to reinvent AI inference infrastructure from the ground up. We’re building a next-generation inference platform that delivers unprecedented efficiency by tightly integrating machine learning, software stack, and custom hardware. Our philosophy is simple: the best performance comes from holistic co-design, where every layer, from model architecture to kernels to silicon, works in harmony. If you’re excited about pushing AI performance to physical limits, and about shaping the future of large-scale inference, we’d love to meet you.\nRole Summary\nWe are looking for a deeply technical AI Compiler & Performance Engineer who thrives at the intersection of ML, compilers, and hardware. In this role, you will design how LLM operations decompose into highly efficient proprietary kernel primitives, optimize execution pipelines, and co-develop abstractions with our hardware and ML teams. This is not a “typical” compiler role, it’s a chance to rethink the entire AI compute stack. At ElastixAI, if improving inference efficiency requires inventing new quantization schemes, rethinking graph-level optimizations, or modifying the hardware ISA, we do it. You’ll have end-to-end ownership to explore radically new ideas and make them real.\nKey Responsibilities\nBreak down LLM and transformer workloads into fine-grained primitives tailored to our proprietary compute hardware.\n\nDesign and implement IR transformations, graph optimizations, kernel lowering, and code generation for novel hardware architectures.\n\nCollaborate with ML researchers to co-design algorithmic optimizations that yield real end-to-end performance gains.\n\nWork closely with hardware architects to refine microarchitectural features, instruction sets, memory hierarchies, and execution models.\n\nBuild performance models, profiling tools, and benchmarking frameworks to identify bottlenecks and guide design decisions.\n\nPrototype and validate improvements across the entire stack - from PyTorch/XLA-level passes to custom kernel implementations.\n\nContribute to shaping the overall system architecture of a first-of-its-kind inference engine.\n\nRequired Qualifications\nBS/MS/PhD in Computer Science, Software Engineering, or a related field.\n\nDeep experience building compilers, optimizing kernels, or working with ML frameworks at a systems level.\n\nStrong proficiency in one or more programming languages such as Python and C++.\n\nStrong understanding of one or more of the following:\n\nLLM architectures and transformer internals\n\nMLIR, LLVM, XLA, TVM, Triton, or similar compiler infrastructures\n\nGPU/TPU/FPGA/ASIC compute models, memory hierarchies, and parallel execution\n\nQuantization, sparsity, or algorithmic optimization for deep learning\n\nDeep expertise on ML frameworks (e.g., PyTorch, TensorFlow, JAX) and understanding of ML model deployment challenges.\n\nSolid understanding of software engineering best practices, including data structures, algorithms, and testing.\n\nThinking in terms of latency, cycles, memory bandwidth, and arithmetic intensity, not just algorithms.\n\nExcellent problem-solving abilities and a knack 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