Mid-Level AI Engineer

Scion Technology has been engaged to conduct a search for an experienced AI Engineer for our client, a pioneering team pushing the boundaries of robotics. This is a full-time position based in North Reading, MA, currently offering a hybrid schedule.

They are seeking exceptional AI/ML Engineers to join their advanced Physical AI engineering team at the forefront of industrial robotic innovation. This role sits at the intersection of cutting‑edge AI technology and real‑world product deployment, where you’ll help define what’s possible in physical AI for both autonomous mobile robots (AMRs) and industrial robotic arms. Their team is building differentiating solutions that push the boundaries of what robots can perceive, understand, and interact with in dynamic real-world environments.

• Work on cutting-edge Physical AI powering real-world robotics, not just research or simulation
• High ownership role with visibility across the full product lifecycle
• Competitive compensation and strong long-term growth potential in a rapidly evolving AI/robotics space
• Be part of shaping the future of intelligent automation across industries

• Design, train, and deploy deep learning models for production robotics applications including object detection, classification, image segmentation, pose estimation, and anomaly detection
• Own features end-to-end from understanding the problem, curating the right data, designing the model, training and evaluating rigorously, deploying to robot hardware, and supporting in production
• Evaluate models with scientific rigor, establish meaningful metrics for your models, systematically analyze performance across training iterations, and maintain clear visibility into what’s improving, what’s regressing, and why
• Work across sensor modalities making informed decisions about how to leverage RGB‑D cameras, LiDAR sensors for different perception tasks, understanding the trade‑offs in performance, cost, and environmental robustness
• Leverage simulation for development and validation; use NVIDIA Isaac Sim and related tools to generate synthetic training data, validate models against edge cases, and accelerate development cycles
• Integrate AI into the robotics stack; write production‑quality Python and C++ code that integrates with ROS2‑based systems, ensuring your AI components are reliable, well‑tested, and maintainable

• Bachelor’s degree (Masters preferred) in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field
• 3+ years of professional experience building and deploying deep learning models, with a strong focus on computer vision (object detection, segmentation, pose estimation, tracking, or related tasks)
• Production deployment experience: you have taken at least one model from training through deployment where real users or customers depend on it. You understand the gap between research and production
• Experience with data engineering for ML: you’ve built or maintained data pipelines, dealt with annotation quality issues, managed dataset versions, and understand that data quality directly determines model quality
• Rigorous approach to model evaluation: you track experiments systematically (Weights & Biases, or similar), understand evaluation metrics and their limitations, and can diagnose why a model is failing

• Experience with robotics perception in production: obstacle detection, navigation, manipulation, or inspection on physical robots
• Experience with sim‑to‑real transfer: domain randomization, domain adaptation, or fine‑tuning strategies to bridge the simulation‑to‑reality gap
• Exposure to reinforcement learning or imitation learning for robot control
• Awareness of Vision‑Language‑Action (VLA) models and emerging foundation model approaches for robotics

This direct hire position offers a salary range of $200,000‑230,000 annually, commensurate with experience. They offer a robust benefits package that includes comprehensive medical, dental, and vision coverage, along with additional perks. Relocation assistance may be available for qualified candidates.