Jobs at 42dot

Design and implement a web-based visualization platform for autonomous driving datasets. Build synchronized multi-modal visualizations (LiDAR, camera, labels, trajectories), optimize frontend rendering for large datasets, improve annotation UI/UX, integrate/customize open-source visualization tools, and ensure production-grade code quality, testing, and documentation while collaborating with perception, planning, and data engineering teams.

Lead design strategy for in-vehicle (IVI) and connected mobile products. Drive end-to-end UX from information architecture and user flows to prototyping and visual design. Collaborate with product, engineering, and research teams, improve design systems, represent design in roadmap and implementation decisions, and provide design leadership and mentorship.

Lead design and implementation of a scalable data lakehouse and ML training/evaluation platform for autonomous driving. Build data SDKs, optimize data pipelines and serving, maintain infrastructure (processing pipelines, databases, lakehouse), collaborate with ML and cloud teams, and drive technical strategy to improve model training, validation, and deployment efficiency.

Develop and scale driving foundation models (VLM/VLA) using multimodal sequential data (camera, video, map, trajectory, action, language). Design, train, and evaluate models for scene understanding, temporal reasoning, agent interaction, risk/event understanding, and VLM-based applications (captioning, VQA, auto-labeling). Collaborate with simulation, data, and ML platform teams to integrate models into autonomous driving systems and build large-scale datasets, benchmarks, and training recipes.

Manage and operate large-scale, multi-datacenter GPU clusters using Kubernetes and Slurm. Monitor and diagnose hardware and software failures, develop automation scripts in Python or Shell, manage GPU quotas, support ML researchers, and participate in designing and tuning distributed training environments for large-scale autonomous driving models.

Design and build closed-loop evaluation and simulation pipelines for driving models; mine safety-critical scenarios; improve driving policies via RL/offline-RL, reward modeling, and policy distillation; automate rollouts, metrics, failure analysis, and integrate systems with data and ML platform teams.

Design and build full-stack web applications for visualizing large-scale autonomous driving multi-modal data (LiDAR, camera streams, labels, trajectories). Implement React frontends and Python/Node.js backends, optimize rendering and backend performance, provide data loading/streaming APIs, integrate model outputs and evaluation metrics, customize open-source visualization tools, and maintain production-quality code, tests, and documentation.

Research and develop 3D computer vision and machine learning algorithms for autonomous driving, including 3D shape modeling, pose estimation, tracking, self-supervised representation learning, physics-based vision, SLAM, and world-model simulation. Implement efficient, scalable vision systems and multi-sensor fusion for deployment in large-scale environments.

Build high-fidelity, physics-based simulation environments using 3D generative AI and vehicle dynamics to train, evaluate, and deploy autonomous driving AI. Design scalable closed-loop simulation pipelines, implement and validate vehicle dynamics/kinematics, develop optimized C++/Python/CUDA simulation modules, and integrate perception, planning, and control stacks to minimize the Sim-to-Real gap.

Design, develop, and maintain high-fidelity simulation environments for autonomous driving. Implement vehicle, sensor, and environment models, design validation scenarios for perception/planning/control, integrate simulation tools into development pipelines, analyze results, and propose performance improvements while collaborating across engineering, robotics, and data teams.

Design, develop, optimize, and deploy middleware and core software (real-time communication, execution frameworks) for autonomous driving on Linux/RTOS. Ensure automotive safety (ISO 26262) compliance, build debugging/testing tools, participate across the full SDLC, conduct design/code reviews, and collaborate with application and global cross-functional teams.

Research and develop 3D computer vision and machine learning algorithms for autonomous driving, including 3D shape modeling, pose estimation, tracking, self-supervised representation learning, world models and closed-loop simulation, sensor fusion, and scalable vision system design and optimization.

Design and develop end-to-end planning models for trajectory generation and decision-making, build closed-loop simulation environments and evaluation pipelines, integrate models with simulation and real vehicles, run real-vehicle experiments, and iterate on models using simulation and real-world failure analyses to improve autonomous driving systems.

Design, implement, and optimize advanced control and motion-planning algorithms (MPC, LQR/LQG, SMC, PID) for autonomous vehicles. Develop real-time, safety-critical C++/ROS2 software, integrate learning-based policies and filters, perform vehicle-level validation and parameter tuning, and optimize control systems for stability, ride comfort, and safety.

Optimize and validate deep learning models for GPU/NPU deployment in vehicles. Perform system profiling and low-level CPU/GPU/NPU performance analysis, develop automated performance analysis tools and metrics, and support runtime optimization and deployment of AI models in autonomous driving environments while collaborating with AI and software teams.

The OS Tools Software Engineer will design and implement tools for Vehicle OS, focusing on messaging systems and developer environments, contributing to autonomous driving technology.

Lead delivery of complex hardware, firmware, and software programs for a safety-critical Software-Defined Vehicle platform. Coordinate requirements, run agile cadences and V-model validation, manage suppliers and budgets, and communicate technical status to global stakeholders to meet regulatory and corporate objectives.

Design, build, and maintain automated test frameworks and pipelines for embedded automotive ECUs and virtual ECUs across SIL/MIL/HIL. Build custom HIL benches, execute and analyze automated tests, verify automotive network protocols, integrate tests into Git-driven CI/CD, and maintain ASPICE-compliant requirement traceability.

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

Develop scalable data pipelines for autonomous driving, enhancing data extraction and labeling processes, and collaborating with cross-functional teams on ML models.