Staff Machine Learning Engineer – BEV/Multi-Modal Perception

Jobtailor; Ann Arbor (MI); On-site; USD 150,000 - 230,000; Full time

Job summary

Jobtailor is seeking a senior leader in perception for autonomous systems in the United States. You will lead BEV model development, design multi-modal fusion architectures, and drive end-to-end perception solutions across camera, LiDAR, and HD maps.

You will own large-scale training pipelines, improve robustness, and mentor ML engineers while pushing forward self-supervised learning and foundation models for 3D perception.

Qualifications

• 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems.
• Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion.
• Strong background in multi-modal sensor fusion, particularly camera and LiDAR data.
• Experience with large-scale data pipelines and distributed training.
• Publications or open-source contributions in top-tier venues.

Responsibilities

• Lead BEV model development and execute the technical roadmap for BEV-based perception models across detection, segmentation, road topology, and scene understanding
• Design multi-modal architectures that fuse camera, LiDAR, radar, and HD maps into unified spatial representations
• Develop foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations
• Own large-scale training workflows, including data sampling, augmentation, distributed training, and hyperparameter optimization
• Improve model robustness and generalization for low visibility, occlusions, and rare scene configurations
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance
• Collaborate with sensor calibration, mapping, and fusion teams on cohesive perception model interfaces
• Mentor and guide ML engineers while cultivating experimentation, code quality, and model validation best practices
• Explore self-supervised learning, large-scale pretraining, and foundation models for 3D perception

Skills

BEV Modeling

3D Scene Understanding

Multi-Modal Sensor Fusion

Deep Learning Frameworks (PyTorch, TF)

Python

Distributed Training

Experiment Management

Mentoring

Education

M.S. or Ph.D. in Computer Science

Electrical Engineering

Robotics

Tools

Ray

TensorFlow

PyTorch

Experiment Management Systems

Job description

• Lead BEV model development and execute the technical roadmap for BEV-based perception models across detection, segmentation, road topology, and scene understanding
• Design multi-modal architectures that fuse camera, LiDAR, radar, and HD maps into unified spatial representations
• Develop foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations
• Own large-scale training workflows, including data sampling, augmentation, distributed training, and hyperparameter optimization
• Improve model robustness and generalization for low visibility, occlusions, and rare scene configurations
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance
• Collaborate with sensor calibration, mapping, and fusion teams on cohesive perception model interfaces
• Mentor and guide ML engineers while cultivating experimentation, code quality, and model validation best practices
• Explore self-supervised learning, large-scale pretraining, and foundation models for 3D perception

Requirements

• 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems
• M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience)
• Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion
• Strong background in multi-modal sensor fusion, particularly integrating camera and LiDAR data
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
• Experience with large-scale data pipelines, distributed training, and experiment management systems
• Demonstrated leadership in driving ML model innovation and mentoring technical teams
• Experience with autonomous driving or robotics perception in production environments
• Experience with MLOps and infrastructure tools (Ray)
• Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling
• Familiarity with 3D labeling, calibration, and sensor simulation pipelines
• Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL)
• Understanding of performance tradeoffs and deployment constraints (latency, memory, accuracy)

Core Competencies

Expertise in BEV model development and multi-modal sensor fusion, with a strong focus on deep learning for perception and 3D vision. Proven ability to lead technical teams, mentor engineers, and drive innovation in autonomous systems.

Highest-signal resume keywords

• BEV Modeling
• 3D Scene Understanding
• Multi-Modal Sensor Fusion
• Deep Learning Frameworks (PyTorch, TensorFlow)

Hard Skills

• Deep Learning for Perception
• 3D Vision
• Large-Scale Data Pipelines
• Distributed Training
• Hyperparameter Optimization
• Model Robustness Improvement
• Self-Supervised Learning
• Spatial-Temporal Modeling
• Camera and LiDAR Integration
• 3D Labeling and Calibration

Soft Skills

• Mentoring
• Collaboration
• Experimentation
• Code Quality
• Model Validation Best Practices

Certifications & Qualifications

• M.S. or Ph.D. in Computer Science
• Electrical Engineering
• Robotics

Industry Keywords

• Autonomous Systems
• Perception Models
• Sensor Calibration
• Mapping and Fusion
• Publications in CVPR, ICCV, NeurIPS, ICRA, CoRL

Tools & Technologies

• Python
• PyTorch
• TensorFlow
• Ray
• Experiment Management Systems