Jobs at APTIV

Leads customer and internal quality activities for new product development and launches. Reviews customer requirements, develops APQP documentation, manages FMEA and control plans, leads quality workshops and audits, drives corrective actions and nonconformance resolution, and supports PPAP, production part approval, and flawless start of production. Partners with cross-functional teams and customers to mitigate quality risks, implement error-proofing, maintain compliance, and achieve continuous process improvement.

Manage full-cycle recruitment for automotive, IT, engineering, and semiconductor roles. Responsibilities include sourcing, screening, interviewing, candidate pipeline development, hiring manager collaboration, onboarding support, recruitment metrics analysis, candidate communication, and embedding DEIA principles throughout the hiring process.

Develop and optimize electrical manufacturing tests, test software, hardware, and application-specific tooling for automotive products. Manage test requirements, coverage analysis, equipment development, supplier buy-offs, capability studies, documentation, and statistical optimization. Support global test teams, provide training, participate in program escalations, and ensure compliance with Aptiv processes and procedures.

Delivers customer-facing engineering solutions for vehicle systems and connected services. Responsibilities include developing requirements, analyzing vehicle-level issues, performing ECU and powertrain diagnostics, acquiring and validating data, supporting simulation and on-vehicle testing, identifying root causes, and coordinating issue resolution. The role also produces engineering reports, presentations, and project timelines while collaborating with customers and cross-functional teams.

Designs and develops multilayer PCBs for high-performance, reliable, high-volume products. Responsibilities include schematic and BOM creation, PCB layout, high-speed interface support, stack-up analysis, SI/EMI/EMC and thermal validation, manufacturing output generation, Valor verification, tooling documentation, and circuit simulations. The role collaborates with global engineering teams, participates in design reviews, supports production documentation, and ensures compliance with Aptiv quality standards.

Support, enhance, and maintain a web-based automotive business intelligence platform. Investigate and resolve production issues across distributed systems and legacy codebases, collaborate with analysts and developers, implement improvements, validate fixes through acceptance testing, and improve system reliability, performance, and development efficiency. The role involves backend development, microservices, software testing, debugging, and technical documentation.

Develops and optimizes electrical manufacturing functional tests, test software, hardware, and tooling for automotive product programs. The role manages test requirements, coverage analysis, equipment procurement and validation, capability studies, statistical analysis, documentation, training, and global test-process support. Requires technical education, programming skills, automotive communication protocol knowledge, statistical methods expertise, and strong problem-solving abilities.

Design, build, test, and operate cloud-based data ingestion and ETL systems. Develop backend services in Golang (with Python where appropriate), run workloads on Kubernetes/AWS, troubleshoot distributed systems, and collaborate to improve scalability, reliability, and performance.

Build, operate, and maintain MLOps platforms on AWS for autonomous driving workloads. Implement multi-zone, highly available training and deployment environments, operate distributed multi-GPU training (Ray), and maintain ML pipelines (Airflow, MLflow). Support CI/CD for ML code and infrastructure, ensure reproducible, traceable, auditable ML workflows, and troubleshoot platform-level issues.

Build and operate MLOps platforms on AWS for autonomous-driving ML workloads. Maintain highly available multi-zone training and deployment environments, run distributed multi-GPU training (e.g., Ray), implement Airflow orchestration and MLflow model lifecycle, support GitHub-based CI/CD for code/models/infrastructure, and ensure reproducible, traceable, auditable ML workflows.

Lead MLOps platform and infrastructure for autonomous-driving ML: build multi‑AZ AWS/Kubernetes environments, operate distributed multi‑GPU training, implement Airflow/MLflow pipelines, CI/CD, data ingestion/validation, monitoring, and collaborate with ML teams to deliver safety‑critical production ML systems.