Applied Machine Learning Engineer – Robotics & Perception Systems

Applied Machine Learning Engineer – Robotics & Perception Systems

Location: Brooklyn, NY (Hybrid)

Industry: Robotics, AI, & Intelligent Automation
Employment Type: Full-Time

Join a mission-driven tech company revolutionizing intelligent robotics for real-world applications. From automated labs to next-generation logistics, this team develops AI-powered robotic platforms designed to interpret and respond to the world around them. They are seeking a skilled Applied Machine Learning Engineer to bridge the gap between theoretical models and real-world robotic performance.

This is an opportunity to work at the intersection of machine learning, perception, and robotics. You’ll build impactful AI systems, partner with a cross-functional team of engineers and scientists, and contribute to the deployment of intelligent machines operating in dynamic environments.

Key Responsibilities:

• Design and optimize machine learning models for perception, motion prediction, and decision-making in robotic systems.
• Collaborate closely with teams in robotics software, hardware, and computer vision to integrate learning-based solutions into real-time systems.
• Build pipelines for preprocessing, labeling, and analyzing multi-sensor datasets (e.g., visual, inertial, and range data).
• Prototype models in simulation and deploy them in production on physical robotic systems.
• Perform experimental validation, error analysis, and performance tuning in lab and field environments.
• Develop scalable, reproducible training pipelines using modern CI/CD and versioning tools.
• Monitor ML advancements and evaluate new techniques for potential integration into the robotics stack.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related field.
• 3+ years of experience building and deploying ML models in applied settings, ideally involving robotics, automation, or real-world sensor data.
• Strong Python skills, with experience in ML libraries such as PyTorch, TensorFlow, and scikit-learn.
• Solid understanding of supervised and unsupervised learning, model generalization, and evaluation metrics.
• Experience with data workflows for large-scale sensor data (camera, lidar, IMU).
• Familiarity with containerization, CI/CD, and version-controlled model training environments.

Preferred Qualifications:

• Experience with robotic middleware frameworks (e.g., ROS or similar).
• Exposure to real-time inference on embedded or edge devices.
• Background in computer vision, reinforcement learning, or SLAM techniques.
• Knowledge of distributed training and multi-node experimentation workflows.

What This Role Offers:

• Impactful work deploying AI in physical systems with real-world users.
• A collaborative, high-caliber team focused on deep tech innovation.
• Access to cutting-edge robotic hardware, lab space, and sensor arrays.
• Competitive compensation including salary, equity, and comprehensive benefits.
• Flexible hybrid work model with support for career advancement and continuous learning.

Ready to help robots see, decide, and act smarter? Apply now to become part of a team reshaping automation with intelligent systems.