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As a Research Engineer, you will integrate ML models into planning stacks, develop testing interfaces, and collaborate with teams to enhance autonomous truck capabilities.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Develop and implement safety framework methodologies for autonomous trucking readiness decisions. Build high-signal safety datasets across simulation and closed-course testing, establish feedback loops from road monitoring and safety events, validate safety evaluation pipelines, and create traceable safety artifacts. The role also requires cross-functional collaboration, technical mentorship, data analysis, statistical evaluation, and evidence-based safety case development for autonomous vehicles.

Build and deploy internal AI tooling, agents, integrations, and workflows that improve productivity across engineering, research, and business teams. Evaluate emerging AI tools and models, develop observability and responsible-use guardrails, and partner with stakeholders to prototype and scale solutions. Drive adoption through documentation, enablement, and hands-on support while integrating AI into autonomy, simulation, ML training, and business workflows.

Own the design, deployment, security, and lifecycle management of Waabi’s multi-site network infrastructure. Responsibilities include switching, routing, wireless, firewalls, ISP planning, incident response, configuration hardening, IP addressing, segmentation, zero-trust access, vehicle connectivity, developer network support, on-premise compute networking, documentation, and disaster-recovery runbooks.

Develop signal processing algorithms for camera, LiDAR, radar, and other autonomous-driving sensor data. Design acquisition, fusion, filtering, and learning-based methods; optimize them for CPU, GPU, DSP, and specialized accelerators; and collaborate with autonomy and hardware teams to deliver reliable, low-latency production systems. The role also supports experimental validation and potential research publications.

Lead software quality for autonomous vehicle planner architecture, developing fault-tolerant systems and improving reliability across motion planning and onboard software teams. Design motion planning software and decision-making algorithms for complex traffic scenarios, while managing experimentation and data-generation pipelines. Collaborate with multidisciplinary engineering and research teams to deliver safe, production-quality self-driving technology.

Responsible for vehicle and component bringup, troubleshooting software and hardware faults, and supporting an autonomous vehicle fleet. Develops and executes on-vehicle test plans, analyzes sensor data, improves system performance and testing efficiency, documents debugging processes, and collaborates with engineering teams and OEMs on communication interfaces and vehicle performance.

Decompose product guidance into use cases, functional requirements, and system architectures for hardware development. Collaborate with multidisciplinary engineers throughout the product lifecycle, author systems engineering artifacts, qualify work products, manage technical risks, and guide safety, security, and quality attribute decisions. Apply systems engineering analysis methods, requirements management, standards decomposition, and testing practices to support reliable autonomous transportation hardware.

Design, build, and own end-to-end 3D asset pipelines and tooling for a multi-sensor simulation stack. Architect formats, storage, validation, packaging, and serving for meshes, neural representations, and generative outputs. Create visualization and curation tools at scale, integrate neural rendering and generative research into production, and collaborate with autonomy and safety teams to meet realism and coverage requirements.