Senior Computer Vision Software Engineer

at  rinftech

Job Description
As a Senior Computer Vision Software Engineer, you will be at the forefront of developing cutting-edge computer vision applications that enable machines to perceive and interpret visual information from images and videos. Your expertise in computer vision algorithms, machine learning, and software engineering will drive the creation of robust and efficient vision systems. Here are the key responsibilities and qualifications for this role.
About us
Rinf.tech is a technology consulting and software services company. Founded in 2006 in Romania, we have grown into a global organization with 600+ engineers and 8 Delivery Centers in Europe and North America (Romania, Bulgaria, Ukraine, Turkiye, and Mexico).
We offer custom software solutions and support for our worldwide partners through three main Business Units: Business Applications, R&D Embedded, Robotics, and Automotive.
At Rinf.tech, you’ll come across friendly people and a genuine way of thinking. RINFers are eager to learn from each other, explore and reinvent the world of technology.
What you’ll be working on
In addition to the traditional outsourcing model, we’ve developed a 100% solution delivery approach where we engage in projects from the early stages, participate in pre-sales discussions, and propose PoCs for bidding. Upon winning bids, we implement the PoC and all its functionalities. Now, we aim to establish an internal AI team for PoC development. This team will work on existing and new PoCs, collaborating closely with architects and Technical Directors from Rinf to deliver them to clients. Our Sales team will leverage these PoCs as a portfolio to drive client acquisition.
Team members won’t be assigned to a single project or business unit but will work across different client divisions (Fintech, Retail, Technology - IoT) as needed. There will be a 1-month ramp-up period to understand the business and competency goals, followed by PoC implementation based on identified needs with Leads/Current Clients. The aim is to secure 2-3 investments within 3 months and transition to project implementation with additional team members, while the initial 6 remain core members focusing on PoCs.

What you’ll do

• Design and implement computer vision algorithms for tasks such as object detection, recognition, tracking, segmentation, and image classification.
• Leverage techniques from machine learning, deep learning, and image processing to achieve accurate and robust results.
• Optimize algorithms for real-time performance on embedded devices or web applications.
• Architect computer vision systems to meet specific requirements and objectives.
• Define system components and workflows for data acquisition, preprocessing, feature extraction, and inference.
• Collaborate with software engineers to integrate computer vision technology into large-scale software systems.
• Train, fine-tune, and optimize computer vision models using machine learning frameworks and deep learning libraries.
• Experiment with different network architectures, loss functions, and optimization techniques to improve model performance.
• Evaluate model performance using appropriate metrics and benchmarks.
• Analyze results and iteratively refine models to achieve desired performance levels.
• Lead research and development efforts related to computer vision.
• Make critical decisions on technology adoption and contribute significantly to advancing computer vision capabilities within the organization

What you offer us

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related fields (qualifications can be overwritten by the relevant experience).
• Minimum 6 years of relevant experience specifically in developing computer vision applications.
• Proficiency in computer vision libraries and frameworks (e.g., OpenCV, TensorFlow, PyTorch, AutoML, Ludwig.ai).
• Understanding of neural network architectures (CNNs, RNNs).
• Strong programming skills in languages such as Python (mandatory), C++, or Java.
• Experience with GPU programming (CUDA, Vulkan, OpenCL) is a plus.
• Knowledge of image processing techniques (filtering, edge detection, feature extraction).
• Familiarity with hardware acceleration (GPUs, FPGAs).
• Analytical mindset with a passion for solving complex vision problems.
• Excellent communication skills to collaborate with interdisciplinary teams.
• Ability to work independently and contribute effectively as a team member.

Considered a plus

• Experience with real-time systems and performance optimization.
• Understanding of 3D vision techniques (stereo vision, depth estimation).
• Version control using Git.

What we offer you

• Flexible working environment
• Learning budget and platforms
• Wide variety of projects you could be part of
• Medical subscription
• HR representative to guide you in your professional career development
• Flexible benefits platform
• Bookster

Our recruitment process

• HR Discussion
• Technical interview
• Offer

Meet us!

If you are still unsure, we are inviting you to come by anytime for a tour of our office without any commitment.

• All applications are strictly confidential. We will not disclose any private information without having your approval.

• Design and implement computer vision algorithms for tasks such as object detection, recognition, tracking, segmentation, and image classification.
• Leverage techniques from machine learning, deep learning, and image processing to achieve accurate and robust results.
• Optimize algorithms for real-time performance on embedded devices or web applications.
• Architect computer vision systems to meet specific requirements and objectives.
• Define system components and workflows for data acquisition, preprocessing, feature extraction, and inference.
• Collaborate with software engineers to integrate computer vision technology into large-scale software systems.
• Train, fine-tune, and optimize computer vision models using machine learning frameworks and deep learning libraries.
• Experiment with different network architectures, loss functions, and optimization techniques to improve model performance.
• Evaluate model performance using appropriate metrics and benchmarks.
• Analyze results and iteratively refine models to achieve desired performance levels.
• Lead research and development efforts related to computer vision.
• Make critical decisions on technology adoption and contribute significantly to advancing computer vision capabilities within the organizatio