Staff Machine Learning Engineer (Technical Lead)

Join Paperless Parts as a Staff Machine Learning Engineer (Technical Lead) and lead the technical execution of a new engineering pod focused on solving complex geometric and document-processing challenges in manufacturing. You will bridge the gap between cutting-edge models and high-velocity engineering execution, driving R&D execution across the entire machine learning lifecycle. This role offers the opportunity to make a high-impact contribution to critical industries like aerospace, defense, and medical devices.

• Act as the bridge between cutting-edge models and high-velocity engineering execution, translating models into production-grade training pipelines.
• Drive R&D execution across the entire machine learning lifecycle, from data labeling strategies to low-latency model inference.
• Lead the technical execution of a new engineering pod tasked with solving complex geometric and document-processing challenges.
• AI Strategist: You understand the trade-offs inherent in technology decisions and think strategically about when to use frontier models, when to train our own, and when to use deterministic solutions
• Mentor & Force Multiplier: You are passionate about teaching and elevating early-career technical talent. You enjoy breaking down complex concepts and foster a culture of engineering discipline and curiosity
• Rigorous yet Pragmatic: You possess a deep theoretical grounding in machine learning and artificial intelligence fundamentals, but you are driven by shipping code that solves real-world, industrial problems. You don’t just apply models; you understand the underlying mathematics, optimization functions, and architectural trade-offs
• Collaborative Partner: You seamlessly collaborate with researchers, other engineering teams, and business stakeholders, helping ensure we build the right technology, deploy it scalably, and bring it to market
• MLOps: Experience working with cloud-native patterns for ML pipelines, including platforms like AWS SageMaker
• AI/ML Fundamentals: A robust understanding of core machine learning and deep learning theory, including neural networks, statistical modeling and inference, and metric learning
• Communication Mastery: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and influence decisions without relying on authority. This may include technical talks and publications
• Advanced Academic Foundation: a technical degree in Computer Science, Applied Mathematics, or closely related field, with a strong understanding of the mathematics behind modern AI/ML techniques is essential. An advanced degree and track record of peer-reviewed publications is a strong plus when paired with proven software experience in industry
• 8+ years of experience in relevant R&D roles with a strong background in SaaS products at scale (start-up to scale-up transition experience preferred)