Top Tech Jobs & Startup Jobs

Lead the strategy, architecture, configuration, integration, governance, and AI automation of Aurora’s HR technology ecosystem. Oversee HRIS, ATS, rewards, and performance platforms; improve data flows across People, Finance, and Commercial systems; establish privacy, compliance, and audit controls; partner with executives and vendors; and build and mentor a high-performing People Technology team.

Lead cybersecurity product strategy and large-scale security programs across Aurora’s software, hardware, and services. Partner with product, engineering, suppliers, and development partners to define security features, manage risks, establish roadmaps, integrate security assurance and governance into product development, and track security KPIs. The role requires influencing cross-functional teams, managing requirements and dependencies, and ensuring products meet cybersecurity standards, particularly in automotive and autonomous vehicle environments.

Leads corporate insurance strategy, policy placements, renewals, broker and carrier negotiations, quantitative risk modeling, enterprise risk assessments, claims management, and governance. Partners with Legal, Finance, and Operations to mitigate operational and financial exposures. Develops risk financing strategies, maintains compliance documentation, and applies AI-driven tools to improve claims processing, reporting, and predictive cost modeling.

Optimize deep learning networks for autonomous vehicles and large-scale data centers. Analyze and troubleshoot performance using profiling and roofline modeling, improve software architecture, latency, GPU memory usage, and throughput, and deploy scalable models with frameworks such as PyTorch or TensorFlow. Collaborate across engineering and research teams on large codebases, applying CUDA, C++, Python, and inference acceleration technologies in Linux/Unix environments.

Research and explore Vision-Language-Action and Vision-Language Model architectures for autonomous driving. Identify model limitations, redesign architectures, translate academic research into scalable production code, optimize models for supercomputing environments, and mentor engineering teams transitioning into this area.

Lead development of online mapping perception algorithms for Aurora’s autonomous driving system. Research and build deep learning models, develop verification and validation techniques, and collaborate with mapping, simulation, and safety teams. Build infrastructure and tests supporting deployment decisions, then integrate, test, and deploy production-ready solutions into the Aurora Driver codebase.

Define vehicle connectivity architecture and build reliable, low-latency vehicle-to-cloud communications for self-driving vehicles. Develop high-performance, thread-safe systems in C++ and Linux, address networking and observability, optimize data throughput, support field testing and fleet operations, mentor engineers, establish coding standards, and guide the connectivity roadmap.

Leads the Hardware Systems Applications Engineering team, setting goals, staffing resources, workflows, and execution processes. Oversees cross-functional handoffs across software, product, operations, and hardware engineering teams. Develops metrics to monitor product hardware health and performance, communicates team and fleet results to leadership, and drives data-driven issue resolution. The role is based on-site in the DFW area with travel to engineering, operations, and remote test locations.

Develop real-time behavior-planning algorithms for autonomous vehicles, selecting safe and comfortable trajectories. Integrate motion planning with machine learning, build production-grade Aurora Driver software, and create metrics and testing workflows for algorithm iteration. The role requires strong Python or C++ programming experience in low-latency or real-time environments and experience productionizing motion-planning algorithms. GPU and machine-learning toolkit expertise is preferred.

Develop and optimize deep learning and machine learning perception models for autonomous vehicles. Address object detection, traffic-actor tracking, action recognition, and semantic scene understanding. Deploy production-ready software to real-time vehicle platforms, build scalable ML pipelines, analyze large datasets, and resolve failures through on-road and simulated testing. Collaborate with cross-functional engineering teams to deliver robust computer vision systems using 3D sensor data from LiDAR, Radar, and cameras.