Senior Principal Machine Learning Engineer

Location: San Jose, California, United States

Department: Software

Overview

As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, or related field; advanced degree (MS or PhD) strongly preferred
• 8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems
• Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale
• Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art
• Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority
• Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects

Key responsibilities

• Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem
• Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale
• Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures
• Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps
• Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer
• Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud
• Drive resolution of the most complex, ambiguous technical challenges—including those that span organizational boundaries or require novel approaches not yet established in the field