Senior Machine Learning Engineer, Perception

Plus 2; Santa Clara (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Plus 2 seeks a highly skilled Machine Learning Engineer to develop BEV fusion models using camera, LiDAR, and radar for autonomous driving and 3D scene understanding. You will design scalable perception algorithms and work across data, research, and deployment teams to deliver production-ready solutions.

The role requires expertise in Python and deep learning pipelines, with strong background in PyTorch, TensorFlow, or JAX, and hands-on experience with LiDAR-based 3D perception and multimodal

Qualifications

• Ph.D. or Masters in AI, Computer Science, Electrical Engineering, Robotics, or a related field.
• Ph.D. new grad or Masters + 3 years industry experience
• Proficiency in Python and experience building deep learning pipelines.
• Strong expertise in PyTorch, TensorFlow, or JAX.
• Proven experience with LiDAR-based 3D perception and BEV representation models
• Deep understanding of multimodal sensor fusion architectures and techniques.
• Familiarity with camera, LiDAR, and radar modalities and their synchronization, calibration, and integration in perception pipelines.
• Solid foundation in computer vision, deep learning, and 3D geometry.
• Industry or academic experience in autonomous vehicle perception, robotics, or related areas.
• Hands-on experience developing deep learning models in real-world or production environments.
• Experience with distributed training, high-performance computing, or GPU acceleration.

Responsibilities

• Design, implement, and optimize BEV-based perception models that fuse camera, LiDAR, and radar inputs.
• Benchmark perception models using large-scale datasets and well-defined quantitative metrics.
• Collaborate cross-functionally with research, data, and deployment engineers to refine models and support real-world applications.
• Maintain a strong focus on performance, robustness, and scalability for deployment in production systems.
• Ensure that your work is performed in accordance with the companys Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
• Ensure team compliance with QMS, monitor quality, and drive process improvements.

Skills

Python

PyTorch

TensorFlow

JAX

LiDAR 3D Perception

Multimodal Sensor Fusion

Computer Vision

Distributed Training

GPU Acceleration

Autonomous Driving

Education

Ph.D. or Masters in AI/CS/EE/Robotics

Masters + 3 years industry experience or Ph.D. new grad

Job description

Overview

We are seeking a highly skilled Machine Learning Engineer with deep expertise in developing Birdlong? Eye View (BEV) fusion models using multimodal sensor inputs, particularly LiDAR. You will play a central role in designing scalable perception algorithms that integrate data from camera, LiDAR, and radar sensors to support autonomous driving and 3D scene understanding.

Responsibilities

• Design, implement, and optimize BEV-based perception models that fuse camera, LiDAR, and radar inputs.
• Benchmark perception models using large-scale datasets and well-defined quantitative metrics.
• Collaborate cross-functionally with research, data, and deployment engineers to refine models and support real-world applications.
• Maintain a strong focus on performance, robustness, and scalability for deployment in production systems.
• Ensure that your work is performed in accordance with the companys Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
• Ensure team compliance with QMS, monitor quality, and drive process improvements.

Qualifications

• Ph.D. or Masters in AI, Computer Science, Electrical Engineering, Robotics, or a related field.
• Ph.D. new grad or Masters + 3 years industry experience
• Proficiency in Python and experience building deep learning pipelines.
• Strong expertise in PyTorch, TensorFlow, or JAX.
• Proven experience with LiDAR-based 3D perception and BEV representation models
• Deep understanding of multimodal sensor fusion architectures and techniques.
• Familiarity with camera, LiDAR, and radar modalities and their synchronization, calibration, and integration in perception pipelines.
• Solid foundation in computer vision, deep learning, and 3D geometry.
• Industry or academic experience in autonomous vehicle perception, robotics, or related areas.
• Hands-on experience developing deep learning models in real-world or production environments.
• Experience with distributed training, high-performance computing, or GPU acceleration.