Staff Machine Learning Engineer, Technical Lead

Paperless Parts•Boston, MA

7d•Hybrid

About The Position

Paperless Parts is a SaaS startup helping manufacturers quote faster and win more work. This position requires activities that are subject to US Export Control Laws and require US Citizenship or Green Card Holder. 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.

Requirements

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

Nice To Haves

An advanced degree and track record of peer-reviewed publications is a strong plus when paired with proven software experience in industry.

Responsibilities

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.