AI Software Engineer

Location: Seattle, WA (Hybrid - 3 days/week in office)About ElastixAIElastixAI 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.Role SummaryWe’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.Key ResponsibilitiesArchitect, extend, and optimize core components of our AI serving platform for throughput, latency, and scalability.Customize open-source serving frameworks (e.g., vLLM) for proprietary model ingestion and accelerator integration.Develop efficient model partitioning, scheduling, and memory management strategies for multi-device inference.Collaborate with ML engineers on model export and runtime optimization (quantization, graph transforms).Work closely with hardware engineers to influence accelerator interface design and performance tuning.Build APIs and runtime tools enabling flexible, PyTorch-native model deployment on our infrastructure.Profile, debug, and optimize across the full stack — from Python orchestration to C++ kernels and PCIe drivers.Required QualificationsBS/MS/PhD in Computer Science, Electrical/Computer Engineering, or related field.3+ years of professional experience in systems programming, ML infrastructure, or distributed inference.Proficient in C++ and Python, with strong debugging and performance analysis skills.Deep familiarity with one or more LLM serving frameworks (vLLM, SGLang, TensorRT-LLM, DeepSpeed-Inference, etc.).Understanding of model deployment internals — token scheduling, KV caching, batching, and pipelined inference.Comfortable working close to the hardware abstraction layer — CUDA, PCIe, memory management, or runtime scheduling.Strong collaboration and communication skills; ability to work cross-functionally in a fast-paced startup environment.Preferred / BonusExperience with hardware-aware ML optimization, compiler/runtime integration, or accelerator SDKs.Hands-on experience profiling GPU/accelerator workloads.Familiarity with containerized deployments (Docker/Kubernetes).Exposure to distributed systems or large-scale inference clusters.Contributions to open-source ML or serving frameworks.What We Offer:A chance to be a foundational engineer in an innovative AI startupA dynamic and collaborative work environment and the change to have a significant impact on new technologyThe opportunity to work on challenging problems at the intersection of ML, software, and systems.Competitive compensation and startup equity packageComprehensive medical, dental, and vision coverage (100% paid by employer)Life insurance and AD&D Flexible Time Off (FTO)12-paid holidaysPaid parental leaveGym or fitness benefitCommuter benefitWeekly catered lunches in the officeInvestment in employee learning & development