Senior Machine Learning Perception Engineer – Fallback Driving System

Jobtailor; California (MO); On-site; USD 130,000 - 190,000; Full time

Job summary

Jobtailor is seeking an ML Perception Engineer to design and train perception models using camera, lidar, and radar data. You will help build the fallback autonomy perception stack and define robust evaluation metrics.

You will drive experiments, analyze large datasets, and work with Safety, Systems, and Product teams to translate requirements into model specs. Collaboration and mentorship are key parts of the role.

Qualifications

• BS, MS, or PhD in ML, Robotics, CS or related field or equivalent practical experience.

Responsibilities

• Design, train, and evaluate ML perception models for object detection, segmentation, tracking, and motion prediction.
• Develop and maintain secondary perception stack to enable fallback autonomy.
• Define ML success metrics and run systematic experiments to improve performance.
• Analyze large-scale datasets, curate challenging scenarios, and develop labeling strategies.
• Implement training/inference pipelines with pruning, quantization, and distillation.
• Collaborate with software and infra engineers to productionize models.
• Translate system requirements into ML model requirements, metrics, and validation criteria with Safety, Systems Eng., and Product.
• Contribute to verification/validation via offline eval, simulation, hardware-in-the-loop, and on-road testing.
• Participate in code reviews and mentor other engineers.

Skills

Machine Learning Perception Systems

Deep Learning Architectures

ML Frameworks (PyTorch, TensorFlow, JX

Python Proficiency

Data Pipeline Management

Education

BS/MS/PhD in ML/Robotics/CS

Tools

Camera Data

Lidar Data

Radar Data

ROS

C++

Job description

• Design, train, and evaluate ML perception models for object detection, semantic/instance segmentation, tracking, and short-horizon prediction using camera, lidar, and radar data
• Develop and maintain the secondary perception stack that enables the fallback autonomy system to bring the vehicle to a minimal risk condition
• Define ML success metrics and drive systematic experimentation to improve performance
• Analyze large-scale datasets, curate challenging scenarios, and develop data selection and labeling strategies
• Implement efficient training and inference pipelines, including pruning, quantization, and distillation
• Collaborate with software and infrastructure engineers to integrate models into production systems
• Translate system requirements into ML model requirements, metrics, and validation criteria with Safety, Systems Engineering, and Product
• Contribute to verification and validation through offline evaluation, simulation, hardware-in-the-loop, and on-road testing
• Participate in code reviews, promote engineering best practices, and mentor other engineers

Requirements

• BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field, or equivalent practical experience building ML perception systems
• 3–5 years of experience developing ML solutions in perception, prediction, autonomous driving, or related domains
• Strong experience with camera, lidar, and radar data, including preprocessing, synchronization, and fusion
• Deep expertise in convolutional and transformer-based deep learning architectures for 2D/3D object detection, semantic and instance segmentation, multi-object tracking, and motion prediction
• Proficiency in at least one major ML framework such as PyTorch, TensorFlow, or JAX
• Python proficiency for model development, training, and analysis
• Software engineering skills, including C++ or similar languages in large collaborative codebases
• Ability to define ML metrics, design experiments, and systematically improve model performance and robustness
• Experience deploying ML models on embedded or resource-constrained platforms, including optimization and real-time performance tuning is nice to have
• Experience with AV/ADAS perception stacks, robotics, or ROS is nice to have
• Familiarity with safety-critical systems and development practices is nice to have
• Experience with large-scale data pipelines, labeling workflows, and ML experiment management is nice to have

Core Competencies

Demonstrates expertise in designing and evaluating ML perception models for object detection and tracking, utilizing camera, lidar, and radar data. Proficient in developing efficient training pipelines and collaborating with cross-functional teams to integrate models into production systems.

Highest-signal resume keywords

• Machine Learning Perception Systems
• Deep Learning Architectures
• ML Frameworks (PyTorch, TensorFlow, JAX)
• Python Proficiency
• Data Pipeline Management

ATS Optimization Keywords

Hard Skills

• Object Detection
• Semantic Segmentation
• Instance Segmentation
• Multi-Object Tracking
• Motion Prediction
• Model Evaluation
• Model Optimization
• Data Curation
• Experiment Design
• Model Deployment

Soft Skills

• Collaboration
• Mentoring
• Problem-Solving

Industry Keywords

• Autonomous Driving
• ADAS
• Safety-Critical Systems
• Large-Scale Datasets
• ML Experiment Management

Tools & Technologies

• Camera Data
• Lidar Data
• Radar Data
• ROS
• C++