Top Tech Jobs & Startup Jobs

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.