Jobs at 42dot

Design and evolve public REST and streaming APIs (OpenAPI), implement production frontend in React+TypeScript, integrate 3D glTF assets via three.js/react-three-fiber, build typed clients, runtime validation, mocks and contract tests, ensure streaming UI resilience, and collaborate with partners and designers on API and UX.

Lead migration of a Docker-based virtual vehicle development platform to a hosted, multi-tenant cloud offering. Operate and secure the cloud platform, manage build/release pipelines and multi-architecture CI, maintain observability and test infrastructure, provision partner-facing instances, and champion developer experience and cross-team alignment.

Lead development of machine learning models for autonomous driving, focusing on deep learning and optimization techniques for real-world application. Collaborate with teams and mentor junior engineers.

The Senior Control Systems Engineer designs, implements, and optimizes control algorithms for autonomous driving, collaborating with teams to enhance vehicle performance and safety.

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.

The ML Performance Optimization Engineer optimizes AI models for autonomous driving within vehicle environments, focusing on GPU/NPU-based performance improvements, system profiling, and collaboration with software teams for deployment and performance analysis automation.

The Sr. Staff DevOps Engineer owns infrastructure for the V&V engine, architects CI/CD pipelines, manages build systems, and integrates global systems for high-velocity firmware delivery.

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.

Design and develop closed-loop evaluation and simulation frameworks to assess and improve driving foundation and autonomous driving decision models using RL, reward modeling, policy distillation, rollout automation, failure analysis, and integration with data and ML platform teams to raise safety, comfort, and generalization.

Design and develop vision-language-action driving foundation models using multimodal sequential data. Train and evaluate large-scale models for driving scene understanding, temporal reasoning, agent interaction, and risk/event understanding. Build VLM-based applications (captioning, VQA, auto-labeling), construct multimodal datasets, design training recipes and benchmarks, run ablations, and collaborate with Simulation, Data, and ML Platform teams to integrate models into autonomous driving systems.

Design and develop web-based visualization platforms for autonomous driving datasets, focusing on rendering and analyzing multi-modal data with full-stack applications.

The Frontend Developer designs and develops web applications for autonomous driving data visualization. Responsibilities include optimizing UI/UX, collaborating across teams, integrating open-source tools, and maintaining code quality.

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.

The Senior Computer Vision Engineer will research and develop autonomous driving technologies, focusing on computer vision and machine learning algorithms for 3D shape modeling, object pose tracking, and simulation.

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.

The ML Platform Engineer will develop a scalable data platform for ML training and evaluation, enhance data pipelines, and collaborate with cross-functional teams in autonomous driving projects.

As a Simulation Engineer, you will develop simulation environments for autonomous driving systems and validate their performance through experiments with cross-functional teams.

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

The Senior ML Platform Engineer will develop scalable data platforms for machine learning in autonomous driving, focusing on performance optimization and collaboration across teams.