Staff Machine Learning Engineer, Technical Lead

About this role

<div class="content-intro"><p data-renderer-start-pos="466"></p> <div class="c-message_kit__blocks c-message_kit__blocks--rich_text"> <div class="c-message__message_blocks c-message__message_blocks--rich_text" data-qa="message-text"> <div class="p-block_kit_renderer" data-qa="block-kit-renderer"> <div class="p-block_kit_renderer__block_wrapper p-block_kit_renderer__block_wrapper--first"> <div class="p-rich_text_block"> <div class="p-rich_text_section">Paperless Parts is a SaaS startup helping manufacturers quote faster and win more work. From rockets to medical devices, we power the parts that move the world forward.</div> <div class="p-rich_text_section">&nbsp;</div> <div class="p-rich_text_section"><em data-stringify-type="italic">This position requires activities that are subject to US Export Control Laws and require US Citizenship or Green Card Holder.</em></div> </div> </div> </div> </div> </div></div><p><strong>About the Role</strong>&nbsp;</p> <p>Step into the role of Technical Lead for our newly formed engineering pod, building the core manufacturing intelligence engine powering the future of Paperless Parts. In this high-leverage role, you will act as the bridge between the art of the possible and high-velocity engineering execution. You will translate cutting-edge models and algorithms into production-grade training pipelines and inference services.</p> <p>As the technical anchor of a lean, ambitious team, you will drive R&amp;D execution across our entire machine learning lifecycle—from data labeling strategies to low-latency model inference. You will ensure that our approach to computer vision, document intelligence, and predictive modeling is both mathematically rigorous and operationally sound.</p> <p><strong>Who You Are</strong></p> <ul> <li><strong>Rigorous yet Pragmatic:</strong> 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.</li> <li><strong>Mentor &amp; Force Multiplier:</strong> 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.</li> <li><strong>AI Strategist:</strong> 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.</li> <li><strong>Collaborative Partner:</strong> 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.</li> </ul> <p><strong>Why You'll Love Working Here</strong></p> <ul> <li><strong>R&amp;D Ownership:</strong> You will lead the technical execution of a new, highly visible engineering pod tasked with solving some of the toughest geometric and document-processing challenges in tech.</li> <li><strong>Complex Data &amp; Unique Problems:</strong> Our engineering team deals with rich data, including 2D drawings and 3D CAD models. Your work will change what’s possible in manufacturing.</li> <li><strong>A Culture of Rigor and Velocity:</strong> We value intentionality, persistence, and deep technical discipline. You will have the freedom to meld academic-level inquiry with the agility of a fast-scaling startup.</li> <li><strong>High-Impact Mission:</strong> Our platform powers manufacturing for critical industries like aerospace, defense, and medical devices. The models your team deploys directly impact the physical creation of products that move the world forward.</li> </ul> <p><strong>What You'll Do</strong></p> <ul> <li><strong>Drive R&amp;D Execution: </strong>Own planning and execution of the AI/ML pod’s backlog. Partner closely with the Chief Scientist and other engineering pods to ensure the research pipeline aligns smoothly with border product timelines.</li> <li><strong>Prototype and Transition: </strong>Lead the hands-on prototyping of novel solutions and transition of successful proofs-of-concept into production-ready services. Guide the strategic migration of workloads, identifying opportunities to shift repetitive tasks from expensive frontier models to fine-tuned, open-source architectures.</li> <li><strong>Operationalize ML Infrastructure: </strong>Develop scalable, repeatable approaches to labeling data, training models, and deploying services that support our products with AI capabilities.</li> <li><strong>Design Rigorous Benchmarks: </strong>Define and track metrics that evaluate the effectiveness and costs of our AI-powered solutions, enabling key technology decisions to be data-driven.</li> <li><strong>Mentor the Pod: </strong>Act as the technical anchor and primary mentor for early-career ML engineers. Cultivate an engineering culture of deep theoretical and practical rigor through hands-on pairing and comprehensive design and code reviews.</li> </ul> <p><strong>What You'll Bring</strong></p> <ul> <li><strong>8+ years of experience</strong> in relevant R&amp;D roles with a strong background in SaaS products at scale (start-up to scale-up transition experience preferred).</li> <li><strong>Advanced Academic Foundation:</strong> 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.</li> <li><strong>AI/ML Fundamentals:</strong> A robust understanding of core machine learning and deep learning theory, including neural networks, statistical modeling and inference, and metric learning.</li> <li><strong>MLOps:</strong> Experience working