Job Title: Systems Engineer – Software-Defined Vehicles & AI Infrastructure

We are seeking a high-caliber Young Professional (1–5 years of experience) to join our development team. This role is tailored for an engineer who has bypassed traditional, repetitive coding tracks and moved directly into system stewardship, AI orchestration, and edge-to-cloud architecture.

You will bridge the gap between cloud-native software and safety-critical automotive systems. We value proven capability over decades of tenure. We require a strong academic foundation paired with hands-on practical execution—demonstrated through production-grade Proof-of-Concepts (POCs), personal engineering portfolios, or contributions to relevant open-source repositories.

Core Responsibilities

• AI Orchestration & Supervision: Design and deploy multi-agent AI workflows to automate engineering processes, while acting as the technical supervisor to review, benchmark, and validate AI-generated code for security flaws.
• Prototype & System Integration: Rapidly build, test, and scale functional POCs linking automotive hardware/simulators to high-throughput cloud endpoints.
• Edge Data & Telemetry Tracking: Configure, manage, and scale telemetry pipelines to capture, trace, and monitor live simulation data from connected vehicle components.
• Simulation Testing & Validation: Run autonomous driving and sensor-fusion algorithms through virtual Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) simulation environments.

Job Requirements: Qualifications, Portfolio, & Skills

1. Practical Experience & Portfolio

• Professional Experience: 1 to 5 years of professional software engineering experience, ideally working in fast-paced tech startups, R&D labs, or innovative automotive groups.
• Demonstrated Track Record (Preferred exposure to at least two of the following):
  • Code contributions to open-source repositories (specifically within AI, robotics, or cloud-native domains).
  • Personal POCs or system architectures demonstrating multi-agent AI orchestrations.
  • Experience building or launching software that connects physical edge hardware to cloud environments.

2. Technical Skills & Tools

• Languages: Strong fundamentals in Rust or Modern C++ (17/20/23) for performance-critical tasks, alongside Python for scripts and data pipelines.
• AI Tooling: Hands-on experience building applications using LLM frameworks like LangChain/LangGraph, CrewAI/AutoGen, or vector databases.
• Systems & Middleware: Practical experience working with ROS 2, Linux/Real-Time Linux environments, or basic AUTOSAR concepts.
• Cloud & Observability: Working knowledge of containerization (Docker/Kubernetes), modern CI/CD automation pipelines, and observability tools like OpenTelemetry.

3. Academic Background

• Bachelor’s or Master’s degree in Computer Science, Robotics, Embedded Systems, Electrical Engineering, or a heavily quantitative field with a strong academic record.

4. Mindset & Personality Traits

• High Learnability & Curiosity: An aggressive drive to self-start, unlearn outdated paradigms, and rapidly master new software stacks.
• Rigorous Analytical Skepticism: A meticulous eye for detail that refuses to trust code (human or AI-generated) without thorough automated testing and verification.
• System-Level Thinking: The ability to see beyond an isolated block of code to understand how it impacts a massive, interconnected physical and digital ecosystem.