Senior Engineer - Algorithms

Aptiv•Troy, MI

21h

About The Position

We are Aptiv - a global technology company with 190,000 specialists in 46 countries. We develop innovative software and build the hardware to bring autonomous driving cars, advanced driver-assistance systems, connected vehicles and smart cities to life in a way that only we can. We work in partnership with almost all car manufacturers. Our sensors, systems and software can already be found in almost all passenger cars today. With our deep domain expertise, Aptiv is developing solutions that solve our customers' toughest challenges. We are enabling the transition to software-defined vehicles supported by electrified and intelligently connected architectures – which will combine to power the future of mobility. Why join Aptiv? You'll have the opportunity to work on cutting-edge applications, develop breakthrough technologies, and deliver innovative solutions to some of the world’s leading automotive brands. See your work come to life on the road—helping make mobility safer, greener, and more connected. Ready to shape the future of mobility with us? Summary We are seeking an experienced Machine Learning and Autonomous Driving Algorithms Engineer to develop, integrate, validate, and deploy production software for parking assistance and low-speed autonomous-driving systems. The role requires strong expertise in machine learning, Python software development, scalable data and training pipelines, and algorithm validation in real-world environments. The engineer will work closely with algorithm, software, systems, validation, and vehicle integration teams to solve challenging perception, localization, sensor-fusion, state-estimation, threat-assessment, and planning problems. This position will support global L0-L3 autonomous-driving programs and drive technical initiatives from concept through production deployment while following a rigorous V-cycle and safety-focused development process.

Requirements

Master's degree in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, Machine Learning, Applied Mathematics, or equivalent.
8+ years of industry experience developing production software and algorithms.
PhD may substitute for a portion of experience.
5+ years hands-on experience developing and deploying machine learning algorithms.
Strong understanding of: supervised learning, deep learning, model evaluation, dataset development, error analysis.
Experience with PyTorch and/or TensorFlow.
Strong Python development experience.
Experience building scalable software systems.
Experience working in Linux development environments.
Proficiency with Git-based development workflows and CI/CD practices.
Experience designing, implementing, or maintaining ML training pipelines.
Experience with large-scale datasets and data processing workflows.
Experience performing root-cause analysis of model failures and performance regressions.
Experience working with algorithm, software, systems, and validation teams.
Demonstrated ability to drive technical initiatives from concept through production deployment.

Nice To Haves

Experience developing Transformer-based neural networks.
Experience with: Vision Transformers, Multi-modal architectures, Foundation Models, Generative AI techniques.
Experience optimizing and scaling large-model training.
Experience developing perception, prediction, localization, planning, or sensor-fusion algorithms.
Experience working with: camera, radar, lidar, ultrasonic, vehicle signals.
Experience shipping production ADAS or autonomous-driving features.
Experience with AWS or cloud-native ML infrastructure.
Experience with: experiment tracking, model registry, automated retraining, model deployment pipelines, continuous evaluation.
Experience building scalable ML platforms used by multiple teams.
Experience with: annotation workflows, active learning, synthetic data generation, data quality metrics, scenario mining.
Experience improving model performance through data strategy rather than architecture changes alone.
Experience with: sensor fusion, probabilistic estimation, Kalman filtering, SLAM, factor graph optimization.

Responsibilities

Develop perception, localization, prediction, planning, sensor-fusion, state-estimation, and threat-assessment algorithms for autonomous-driving and parking functions.
Design, integrate, test, and release production software and algorithms for internal and external customers.
Develop and deploy machine-learning algorithms using Python and frameworks such as PyTorch and/or TensorFlow.
Design, implement, and maintain scalable ML training, evaluation, and data-processing pipelines.
Work with large-scale datasets to improve model performance, robustness, and generalization.
Develop and optimize modern deep-learning models, including Transformer-based neural networks.
Validate algorithms through simulation, laboratory testing, proving-ground evaluation, real-vehicle testing, and real-road testing.
Improve algorithm performance across diverse environments, operating conditions, vehicle platforms, and global customer programs.
Perform root-cause analysis of model failures, missed detections, false detections, performance regressions, and safety-related issues.
Use data-centric methods, including data-quality improvement, dataset balancing, scenario coverage, labeling strategy, and targeted data collection, to improve model performance.
Develop algorithms for parking-space recognition, parking-line detection, object recognition, obstacle detection, localization, vehicle alignment, and parking control.
Support state estimation and threat assessment for safe and reliable autonomous-driving operations.
Investigate challenging perception and sensor-fusion problems involving cameras, ultrasonic sensors, radar, lidar, and other vehicle sensors.
Design and execute functional-performance tests and confirm compliance with system and customer requirements.
Apply a disciplined V-cycle development process to support safety-critical automotive software and electronics.
Collaborate with downstream software, systems, validation, and integration teams to support globally deployed L0-L3 autonomous-driving systems.
Work in Linux development environments using Git-based workflows, code reviews, automated testing, and CI/CD practices.
Support cloud-native or AWS-based ML infrastructure and scalable platforms used by multiple engineering teams.
Drive technical initiatives from initial concept and experimentation through algorithm maturation, vehicle integration, validation, and production deployment.
Communicate technical findings clearly and work analytically, creatively, and collaboratively to resolve complex engineering problems