Jobs at 42dot

Develop, optimize, and maintain Linux-based in-vehicle applications for infotainment and autonomous systems. Integrate with hardware/firmware, debug kernel and user-space issues, improve performance and boot times, contribute to system architecture, and collaborate with QA for test strategies and high code quality.

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 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.

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.

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.

Design, implement, and optimize advanced control and motion-planning algorithms (MPC, LQR/LQG, SMC, PID) for autonomous vehicles. Develop real-time, safety-critical C++ software on ROS2, integrate learning-based policies and filters, validate performance on vehicle hardware, and tune parameters to maximize stability, ride comfort, and safety.

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.

Design, build, and operate high-throughput, multi-region data pipelines to deliver petabyte-scale data to GPU infrastructure. Optimize Databricks/Spark workloads, deploy containerized components on Kubernetes, manage ingestion with Kafka, orchestrate workflows with Airflow, and collaborate with AI researchers to feed training and evaluation pipelines.

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.

Serve as the technical bridge between 42dot Pleos Playground and 3rd-party developers: manage SDK/API onboarding, troubleshoot validation failures, act as technical SPOC across internal teams and OEMs, enforce safety/security guidelines, and provide in-vehicle validation and launch support for partner apps.

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.

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 and maintain a scalable data lakehouse and ML training/evaluation platform for autonomous driving. Develop data SDKs, orchestrate pipelines, optimize performance, and collaborate with ML and Cloud teams to support model development lifecycle.

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.

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.

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 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.

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.