AI Software Engineer

Location:  Seattle, WA (Hybrid - 3 days/week in office)  About ElastixAI ElastixAI 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 Summary We’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 Responsibilities Architect, 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 Qualifications BS/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 / Bonus Experience 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 startup A dynamic and collaborative work environment and the change to have a significant impact on new technology The opportunity to work on challenging problems at the intersection of ML, software, and systems. Competitive compensation and startup equity package Comprehensive medical, dental, and vision coverage (100% paid by employer) Life insurance and AD&D  Flexible Time Off (FTO) 12-paid holidays Paid parental leave Gym or fitness benefit Commuter benefit Weekly catered lunches in the office Investment in employee learning & development