{"id":48910,"url":"https://alion.io/job/elastix-ai-software-engineer","title":"AI Software 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":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":273000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":451},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-02-19T03:28:26Z","employer_posted_date":"2026-02-19","last_verified_at":"2026-10-16T20:20:11Z","board_verified":true,"closed_at":null,"days_open":239,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":239},"description":"About Elastix AI\nWe are building the next-gen AI inference platform.\nDescription\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 building the next-generation AI inference infrastructure - co-designed across ML software and custom accelerator hardware. Our platform dynamically optimizes inference efficiency and scalability across diverse deployments, enabling adaptive, high-performance AI serving.\nRole Summary\nWe’re looking for a systems-minded AI Software Engineer to join our core inference platform team. You’ll design and extend the low-level serving stack - hacking open-source frameworks like vLLM, SGLang, and TensorRT-LLM, building new model sharding and scheduling logic, and integrating deeply with our proprietary AI accelerator. This role sits at the intersection of ML systems, compiler/runtime engineering, and hardware-software co-design.\nKey Responsibilities\nArchitect, extend, and optimize core components of our AI serving platform for throughput, latency, and scalability.\n\nCustomize open-source serving frameworks (e.g., vLLM) for proprietary model ingestion and accelerator integration.\n\nDevelop efficient model partitioning, scheduling, and memory management strategies for multi-device inference.\n\nCollaborate with ML engineers on model export and runtime optimization (quantization, graph transforms).\n\nWork closely with hardware engineers to influence accelerator interface design and performance tuning.\n\nBuild APIs and runtime tools enabling flexible, PyTorch-native model deployment on our infrastructure.\n\nProfile, debug, and optimize across the full stack - from Python orchestration to C++ kernels and PCIe drivers.\n\nRequired Qualifications\nBS/MS/PhD in Computer Science, Electrical/Computer Engineering, or related field.\n\n3+ years of professional experience in systems programming, ML infrastructure, or distributed inference.\n\nProficient in C++ and Python, with strong debugging and performance analysis skills.\n\nDeep familiarity with one or more LLM serving frameworks (vLLM, SGLang, TensorRT-LLM, DeepSpeed-Inference, etc.).\n\nUnderstanding of model deployment internals - token scheduling, KV caching, batching, and pipelined inference.\n\nComfortable working close to the hardware abstraction layer - CUDA, PCIe, memory management, or runtime scheduling.\n\nStrong collaboration and communication skills; 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