Senior/Lead Embedded Software Engineer (Edge AI/Computer Vision)

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.