Hardware Design Engineer, AI Inference Engine

ABOUT ELASTIX AI We are building the next-gen AI inference platform. DESCRIPTION Location: Seattle, WA (Hybrid - 3 days/week in office) About ElastixAI: ElastixAI is an early-stage startup poised to revolutionize AI inference infrastructure. We are developing a cutting-edge AI inference solution that dramatically improves efficiency through a holistic co-design approach, spanning from machine learning optimizations and a highly specialized software stack to the inference engine and underlying cloud hardware. We believe in providing a customizable and optimal inference experience, much like tailoring a high-performance computing system to specific needs. Role Summary: We are seeking a visionary and hands-on Hardware Design Engineer to contribute to the design, definition, and implementation of our core AI inference engine. This is a deeply technical role where you will be instrumental in translating AI into a highly efficient hardware design. You will be at the center of our co-design philosophy, working to ensure our inference engine is perfectly harmonized with our ML strategies, software stack, and cloud hardware targets to deliver unparalleled performance and efficiency for next-generation AI models. Key Responsibilities: • Contribute to the architectural definition, design, and implementation of a novel AI inference engine optimized for our specific ML workloads. • Collaborate closely with ML engineers to understand and influence ML directions • Work hand-in-hand with software engineers to define a seamless hardware-software interface, ensuring the inference engine is highly programmable, efficient, and easy to integrate into our broader software stack and compiler. • Partner with cloud engineers to ensure the inference engine architecture aligns with target cloud hardware capabilities, deployment strategies, and performance/cost objectives. • Model and analyze the performance, power, and area (PPA) trade-offs of different architectural choices. • Stay at the forefront of AI accelerator research, identifying emerging techniques and technologies relevant to our co-design approach. • Contribute to the RTL design, simulation, and verification efforts for the inference engine components. • Drive the hardware roadmap for the inference engine, anticipating future AI model trends and optimization opportunities. • Foster a culture of innovation and technical excellence within a highly interdisciplinary engineering team. Required Qualifications: • BS, MS or PhD in Computer Engineering, Electrical Engineering, or a related field. • Proven experience (5+ years) in hardware design, with a strong focus on designing/implementing hardware for AI/ML acceleration. • Deep understanding of modern AI/ML models, particularly LLMs, and their computational characteristics. • Experience with hardware implementation of ML optimization techniques (e.g., sparsity, quantization, pruning). • Proficiency in Verilog or SystemVerilog for RTL design and simulation. • Strong understanding of memory system architecture, on-chip interconnects, parallel processing, and distributed computing. • Excellent problem-solving skills and the ability to analyze complex systems. • Exceptional communication and interpersonal skills, with a demonstrated ability to work effectively in a highly interdisciplinary environment, collaborating with ML, software, and cloud/systems engineers. • Ability to thrive in a fast-paced, dynamic startup environment with a strong bias for action/execution Preferred/Bonus Qualifications: • Knowledge of compiler technologies for AI models (e.g., MLIR, TVM). • Familiarity with performance modeling and analysis tools. • Experience with system-level integration and debugging. • Contributions to relevant research publications or open-source projects. • Understanding of cloud computing environments and deploying hardware accelerators in the cloud. • High-speed inter-chip networking experience 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) • Flexible Time Off (FTO) • Paid parental leave • Company sponsored 401K Plan • Gym or fitness benefit • Commuter benefit • Investment in employee learning & development