Machine Learning / Computer Vision Engineer, Parallel Systems

• Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a closely related technical field
• 1 to 2 years of experience building machine learning systems or exceptional new graduates with strong internships or research experience
• Experience developing machine learning models using PyTorch or TensorFlow
• Strong programming ability in Python
• Solid mathematical foundation in linear algebra, probability, and optimization
• Experience working with real-world datasets in computer vision, robotics, or machine learning

Desirable

• We are looking for an early-career Machine Learning / Computer Vision Engineer to help develop the perception systems that power our autonomous platform. You will work on real-world computer vision problems involving multi-sensor data, large-scale datasets, and real-time inference constraints
• This role is ideal for engineers who have strong foundations in machine learning and computer vision and who have already built meaningful ML systems through research, internships, or early industry experience. You will work closely with experienced engineers across autonomy, robotics, and systems to design and deploy perception models that operate on real vehicles
• Training and improving deep learning models for perception tasks including detection, segmentation, tracking, and scene understanding
• Developing scalable training pipelines for large perception datasets
• Working with multimodal sensor data including cameras, lidar, and radar
• Evaluating and adapting state-of-the-art research for real-world deployment
• Improving inference performance and model reliability under real-world conditions
• Supporting deployment of perception models to edge compute systems on autonomous vehicles
• After 30 Days: You understand the fundamentals of Parallel’s perception stack, datasets, and model training workflows. You are contributing to model training experiments and supporting improvements to the ML pipeline
• After 60 Days: You are independently training and evaluating models for a defined perception task and contributing improvements to model performance or data workflows
• After 90 Days: You have contributed a measurable improvement to an existing perception model or training pipeline and are actively supporting deployment and testing efforts
• Work on real autonomous systems operating in the physical world
• Build perception systems that must perform reliably in safety-critical environments
• Collaborate with engineers across autonomy, robotics, and systems engineering
• Contribute directly to a new class of autonomous transportation platform