Embedded Engineer

We are looking for a Senior/Lead Embedded Software Engineer (Ukraine-based) to join Ralabs and work on technically challenging projects involving embedded systems, edge computing, and AI-powered solutions.

Engagement: Initially, approximately 10–20 hours per week during the first 3 months, with a transition to full-time afterward.

What is the role about?

The role combines hands-on software development, hardware integration, and performance optimization. You will work with resource-constrained devices, adapt existing software to new hardware platforms, and help develop reliable, production-ready solutions.

Beyond individual projects, you will contribute to our broader engineering capabilities in embedded systems and edge AI.

How many stages of the interview are there?

• Interview with the Recruiter – up to 30 min.
• Technical Interview  – up to 1 hour;
• Final interview  – up to 1 hour;
  
  Hard skills
• 6+ years of commercial software engineering experience.
• Proven experience developing software for embedded Linux environments.
• Hands-on experience with ARM-based computing platforms.
• Proven experience integrating software with hardware components and external devices.
• Proven experience optimizing software for performance, memory, and hardware constraints.
• Ability to independently investigate technical challenges and deliver working solutions.

• Upper-Intermediate or higher English proficiency.
   

  Technical Expertise

• Strong proficiency in C/C++ and/or Python.
• Solid knowledge of Linux internals, system-level programming, and debugging.
• Experience with embedded platforms such as NVIDIA Jetson, Raspberry Pi, or similar.
• Experience with YOLO-NAS, YOLOX, and DarkYOLO in ONNX.
• Understanding of hardware interfaces and communication protocols (UART, GPIO, SPI, I2C, etc.).
• Experience with computer vision, image processing, or real-time data processing.
• Understanding of ML model deployment and optimization on edge devices.
• Familiarity with software security, encryption, and secure deployment practices.

• Strong understanding of performance optimization, testing, and troubleshooting.

  
  Leadership & Coloboration

• Ability to take technical ownership of assigned tasks and solutions.
• Strong analytical and problem-solving skills.
• Ability to evaluate technical approaches and make practical engineering decisions.
• Comfortable working with incomplete requirements and exploring alternative solutions.

• Ability to collaborate with cross-functional engineering teams and technical stakeholders.
  
  Responsibilities
   

  Embedded Software Development

• Design, develop, and maintain software for embedded computing platforms.
• Adapt existing software solutions to different hardware architectures.
• Integrate software with hardware components, peripherals, and external systems.
• Troubleshoot system-level issues and improve overall reliability.

• Ensure software operates efficiently within hardware limitations.
   

  Edge AI & Computer Vision

• Support the integration and deployment of AI/ML models on edge devices.
• Optimize inference performance, resource usage, and processing efficiency.
• Work with computer vision and real-time data processing pipelines.

• Evaluate technical approaches for improving system performance.
   

  Engineering Excellence

• Write clean, maintainable, and well-tested code.
• Establish reliable development, testing, and deployment practices.
• Identify performance bottlenecks and propose improvements.
• Ensure appropriate security and reliability standards.

• Maintain technical documentation.
   

  Collaboration & Delivery Support

• Work closely with engineering teams, technical leads, and project stakeholders.
• Participate in technical discovery and solution design.
• Support technical estimations and feasibility assessments.
• Communicate technical challenges, risks, and dependencies.

• Take ownership of delivering working, production-ready solutions.
   

  Pre-Sales & Solution Design (Optional Involvement)

• Support technical discovery for new embedded and edge AI opportunities.
• Evaluate technical feasibility and potential implementation approaches.
• Contribute to solution design and effort estimation.
• Participate in technical discussions with prospective clients.
  
  Nice to Have
• Experience with CUDA, TensorRT, or other NVIDIA technologies.
• Experience with OpenCV, GStreamer, or similar frameworks.
• Practical experience deploying and optimizing ML/CV models on embedded hardware.
• Experience with real-time systems and video processing.
• Experience with secure boot, firmware protection, or embedded security.
• Experience in hardware prototyping and working directly with electronic components.
• Experience with hardware migration or embedded platform modernization.