Role Summary:

The Staff Engineer, Machine Learning, is responsible for leading the development of a single-stage, end-to-end driving model for our Level 2++ to Level 4 Automated Driving stacks. This role leads design, implementation and validation of reinforcement learning-based models using a world-model simulation environment and leverages multi-modal sensor inputs such as camera and radar data to generate driving trajectories.

This role focuses on bridging advances in multi-modal foundation models with the practical challenges of real-time, safety critical embedded deployment. The Staff Engineer, Machine Learning ensures the model is robust, generalizes well, and meets safety standards across a wide range of driving scenarios. This role works closely with embedded engineers, data engineers, and MLOps/DevOps engineers, to create a scalable, high-performance system that delivers real-world impact.

Role Responsibilities:

Model Architecture & Training Strategy

• Research, evaluate, and decide single-stage, end-to-end ADAS model approaches and architectures
• Design and train state-of-the-art end-to-end machine learning models for the ADAS stack
• Define and evolve single-stage training strategies for end-to-end models in collaboration with data engineering and MLOps teams

Reinforcement Learning & Multimodal Modeling

• Oversee the build-up and optimization of a simulation-based reinforcement learning framework
• Train models using reinforcement learning approaches within simulation or world-model environments and reinforcement learning frameworks
• Work with real and synthetic multi-modal sensor data (camera, radar, lidar) to design models that effectively leverage all available data modalities
• Ensure models generalize across diverse driving scenarios and operational conditions

Evaluation, Deployment & Optimization

• Evaluate and benchmark models against real-world driving use cases using scalable evaluation pipelines
• Collaborate with embedded engineering teams to support model optimization, deployment on embedded hardware, and system integration
• Support model integration, performance tuning, and issue resolution during deployment and validation phase

Technical Collaboration & Continuous Improvement

• Partner with embedded, data, and platform teams to align model development with system constraints and deployment requirements
• Share technical insights and lessons learned to improve overall ADAS machine learning development practices

General Skills:

• Deep knowledge in End2End-AI models for automated driving functionalities
• Strong software engineering skills, including the ability to write clean, maintainable, and testable production-quality code
• Strong analytical and debugging skills, with the ability to evaluate tradeoffs and select appropriate technical solutions
• Ability to independently work on moderately complex technical problems, exercising sound judgment in ambiguous problem spaces
• Strong written and verbal communication skills, with the ability to clearly explain complex technical concepts to diverse audiences
• Ability to collaborate effectively with multiple teams, including working across geographies and time zones

Required Specialized Skills:

• Deep Learning expertise on foundation models and VLAMs for Automated driving with a background in CNNs, transformers and spatio-temporal models
• Hands on experience with machine learning frameworks such as PyTorch (or equivalent)
• Reinforcement learning experience, including training agents in simulation environments
• Computer vision experience applying modern deep learning techniques such as CNNs, DETR, and vision transformers to real-world problems
• Experience or strong familiarity with state-of-the-art AD/ADAS systems, including end2end driving models, VLAMs, and world models.
• Strong applied foundation in core machine learning principles, with the ability to translate theory into practical model development and evaluation

Desired Skills:

• Familiarity with deep learning model optimization techniques, such as quantization, pruning, and hardware-aware optimization
• Familiarity with inference frameworks such as TensorRT and ONNX Runtime
• Experience working with simulation frameworks for ADAS development
• Experience with multi-modal machine learning models, including camera and radar fusion and other multi-modal architectures such as VLAMs
• Understanding of automotive safety considerations relevant to machine learning-based ADAS systems

Workplace Flexibility:

• Collaborate across time zones; occasional early/late meetings to align with global partners
• Occasional travel as needed for vehicle testing, integration workshops, or demos

Years of Relevant Experience:

• 6+ years of experience in Applied machine learning or deep learning
• 3+ years of experience reinforcement learning, computer vision, or AD/ADAS systems.
• Strong candidates with equivalent industry experience will be considered

Required Education:

• Master's degree in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field

Desired Education:

• PhD in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field

Compensation

Salary range is dependent on factors such as geographical differentials, credentials or certifications, industry-based experience, qualification and training. In the city of Mountain View, California, the salary range for this position is $196,267 - $269,203.

CARIAD, Inc. provides performance-based merits and annual bonus along with a competitive benefits package. Benefits include medical, dental, vision, 401k with employer match and defined contribution plan, short- and long-term disability, basic life and AD&D insurance, employee assistance program, tuition reimbursement and student loan repayment plans, maternity and non-primary caregiver leave, adoption assistance, employee referral program and vacation and paid holidays. We also offer a unique vehicle lease program that covers registration and insurance fees.