Machine Learning Engineer Intern

Public source summary from intern-list.com / Jobright.

Atoms builds Physical AI and real-world robots for industries including food, mining, and transport. The Machine Learning Engineer Intern will help develop and evaluate machine learning models, multimodal perception systems, simulation tools, and low-latency inference pipelines for autonomous transport platforms.

Responsibilities

Collaborate with research engineers to prototype, evaluate, and test emerging Machine Learning and Deep Learning models for trajectory planning and autonomous behavior Help design and evaluate multimodal systems that integrate raw multi-sensor data (Cameras, LiDAR, Radar) to improve spatial-temporal perception Assist in developing interactive world models and simulation tools to re-simulate real-world driving logs and analyze vehicle trajectories Profile and optimize inference pipelines to help run complex models under low latency constraints on vehicle edge hardware Work with data engineering workflows to identify, curate, and structure rare, complex edge cases and long-tail scenarios from physical operations Partner with validation and QA teams to run model releases through simulated scenarios to catch performance regressions and track model behavior

Qualifications: Currently pursuing a BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Data Science, or a related technical field Strong foundation in deep learning concepts and experience with modern frameworks like PyTorch or JAX (through coursework, research, or prior internships) Hands-on programming experience in Python Academic coursework, project experience, or research in one or more relevant domains: Computer Vision, Spatial-Temporal Modeling, Reinforcement Learning, Model Optimization, Model Evaluation, or Data Engineering A passion for solving complex, messy real-world engineering problems and deploying AI into physical systems Familiarity with C++ Familiarity with robotics data structures or multi-sensor processing (Cameras, LiDAR, or Radar)