Staff Machine Learning Engineer, Technical Lead

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.

As the technical anchor of a lean, ambitious team, you will drive R&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.

• 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.
• 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.
• 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.
• 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.

• R&D Ownership: 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.
• Complex Data & Unique Problems: Our engineering team deals with rich data, including 2D drawings and 3D CAD models. Your work will change what’s possible in manufacturing.
• A Culture of Rigor and Velocity: 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.
• High-Impact Mission: 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.

• →Drive R&D Execution: 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.
• →Prototype and Transition: 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.
• →Operationalize ML Infrastructure: Develop scalable, repeatable approaches to labeling data, training models, and deploying services that support our products with AI capabilities.
• →Design Rigorous Benchmarks: Define and track metrics that evaluate the effectiveness and costs of our AI-powered solutions, enabling key technology decisions to be data-driven.
• →Mentor the Pod: 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.

• 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).
• 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.
• AI/ML Fundamentals: A robust understanding of core machine learning and deep learning theory, including neural networks, statistical modeling and inference, and metric learning.
• MLOps: Experience working with cloud-native patterns for ML pipelines, including platforms like AWS SageMaker.
• 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.