# AI Software Engineer at Elastix > Hybrid · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aelastix%3A4adf77d7-f43b-4de7-97de-ee06f490f5e0 (read 2026-11-09). ## Facts (observed) - Company: [Elastix](https://alderhq.app/companies/elastix) - Location text: Seattle - Where it can be done from (inferred): Hybrid · United States (evidence: "Location: Seattle") - Posted: 2026-02-19 (264 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-08 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/elastix/4adf77d7-f43b-4de7-97de-ee06f490f5e0 - Skills listed (inferred): Python, C++, Kubernetes, Docker, PyTorch, LLMs, Distributed systems ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Seattle, n 204): p25 $125k, median $145k, p75 $166k ## Description (the employer's text) ### About Elastix AI We are building the next-gen AI inference platform. ### Description Job Title: Software Engineer, AI Inference Platform Company: ElastixAI, Inc. 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aelastix%3A4adf77d7-f43b-4de7-97de-ee06f490f5e0 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.