Lead Architect, SambaNova Systems

• Strong Software Engineering Background: Proven experience building, testing, and tuning software for distributed, high-performance systems. In-depth knowledge of operating systems and runtime stacks
• Real-Time Operating Systems (RTOS): Hands-on experience with RTOS and system-level software that directly interfaces with hardware
• High-Performance Computing (HPC): Expertise in designing and optimizing systems that handle massive parallel workloads, including machine learning training and inference tasks that involve billions of operations per second
• Low-Level System Understanding: Deep understanding of hardware-software interaction, including registers, device memory management, and the intricacies of accelerator design. Experience working with ASIC accelerators is highly desirable
• Distributed Systems Expertise: Familiarity with distributed systems architecture, including networking, communication protocols, and the challenges of scaling compute resources efficiently
• Toolchain Expertise: Hands-on experience with software development tools such as Git, Jenkins, and Jira, with an ability to drive automation and continuous integration efforts
• Cross-Disciplinary Knowledge: Ability to work at the intersection of hardware and software, designing systems that optimize both performance and reliability

Desirable

• We’re seeking a Lead Architect, Runtime to join our talented Runtime team—a group of engineers who have a proven track record of building software that directly powers advanced AI workloads and scientific computing
• As a key technical leader, you will be responsible for designing and architecting a high-performance, distributed, and scalable software runtime that supports our broad array of data-flow applications, including machine learning training and inference, data processing pipelines (ETL), and HPC applications
• In this role, you will have the opportunity to define and deliver the architecture of our entire runtime stack, driving everything from OS-level integration to performance profiling, networking, and optimization, while working closely with hardware teams to design the most efficient systems
• Architectural Leadership: Lead the design, development, and performance optimization of the software runtime stack, ensuring it meets the high-performance and scalability requirements of ML, AI, and HPC applications
• Runtime System Design: Architect embedded software infrastructure to enable smooth integration of high-level applications with the underlying hardware, including OS interface/integration, partitioned workload orchestration, fault management, and inter-node communication
• Hardware Interaction: Oversee and guide the low-level integration between software and hardware components, ensuring efficient chipset initialization, monitoring, and fault management
• Technical Strategy: Drive the technical direction for the Runtime Engineering team, ensuring the design and implementation of software that delivers performance and scales efficiently with our next-generation AI hardware and platforms based on our Reconfigurable Dataflow Architecture
• Tooling and Profiling: Lead the design and development of tools and performance profilers, empowering customers to configure, deploy, and optimize their workloads on SambaNova’s Datascale systems
• Mentorship and Team Development: Inspire and guide the team to continuously improve development processes, coding standards, and collaboration practices. Foster a culture of excellence, accountability, and technical growth
• Cross-functional Leadership: Collaborate with hardware, software, and product teams to define requirements and ensure seamless integration between hardware and system software components

• Flexible PTO in US
• Parental Leave
• Benefits (medical, dental and vision)
• Flexible Spending Accounts
• 401k/Pensions
• Gym Access
• Flexible Working Hours

Total funding: $1.1bn

SambaNova Systems is a computing startup focused on building machine learning and big data analytics platforms. Its software-defined analytics platform enables optimum performance for any ML training, inference, or analytics models.