ElastixAI develops FPGA-based hardware that dramatically lowers the cost and power consumption of large language model inference compared with GPU solutions.
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