Software engineer jobs in Boston, MA

About the role

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.

What you'll bring

• 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.
• 8+ years of experience in relevant R&D roles with a strong background in SaaS products at scale.
• 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.
• 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.

Benefits