
51 - 200 employees
đŒ Consulting
đŠ Logistics
đ Aerospace
Consulting âą Logistics âą Aerospace
TRAX is the undisputed leading provider of aviation maintenance mobile and cloud ERP solutions. Over 200 customers have purchased the eMRO, Trax M&E, and eMobility apps products for their fleets consisting of various types of aircraft including Airbus, Boeing, Bombardier, Embraer, and rotorcraft. Customers include defense and commercial airlines, as well as Maintenance, Repair, and Overhaul shops. TRAX eMRO and eMobility are positioned at the leading edge of industry developments with products that support digital signatures, paperless Workpacks and manuals, RFID-capable for logistics, Biometric security, off-line capability for mobile apps, web-based solutions, and the ability for its users to work anywhere with easy access to real-time information.
đ„ 13 hours ago
đ”đ± Poland â Remote
đ” zĆ28k - zĆ35k / month
â° Full Time
đĄ Mid-level
đ Senior
đ§âđ» Full-stack Engineer
đ» Ghost score 0%
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51 - 200 employees
đŒ Consulting
đŠ Logistics
đ Aerospace
Consulting âą Logistics âą Aerospace
TRAX is the undisputed leading provider of aviation maintenance mobile and cloud ERP solutions. Over 200 customers have purchased the eMRO, Trax M&E, and eMobility apps products for their fleets consisting of various types of aircraft including Airbus, Boeing, Bombardier, Embraer, and rotorcraft. Customers include defense and commercial airlines, as well as Maintenance, Repair, and Overhaul shops. TRAX eMRO and eMobility are positioned at the leading edge of industry developments with products that support digital signatures, paperless Workpacks and manuals, RFID-capable for logistics, Biometric security, off-line capability for mobile apps, web-based solutions, and the ability for its users to work anywhere with easy access to real-time information.
âą Write clean, maintainable, and fully tested production code âą Participate in code reviews and collaborate using Agile methodologies, Jira, and Git âą Train and evaluate deep learning models and Vision-Language Models for retail scene understanding and product recognition âą Design and implement computer vision and computer graphics features for interactive Augmented Reality workflows âą Apply 2D/3D geometric principles to spatial solutions âą Profile, optimize, and quantize algorithms for latency- and memory-constrained edge hardware âą Work with multimodal LLMs, VLMs, and prompting techniques to create automated recognition tools âą Refine and scale algorithms for efficiency, robustness across store conditions, and global deployment âą Deploy production-grade solutions across thousands of stores globally âą Collaborate in a distributed international team using Agile practices
âą 5+ years of hands-on experience developing and deploying Machine Learning and Deep Learning models in production or applied R&D environments âą Strong understanding of classical and modern computer vision âą Strong linear algebra, calculus, and mathematical foundations for 2D/3D geometry and spatial graphics âą Practical experience with modern LLMs/VLMs and prompt engineering techniques âą High proficiency in Python âą Experience with deep learning frameworks; Keras, TensorFlow, and JAX preferred âą Proficiency with standard software development tools âą Ability to adapt complex academic/R&D concepts into robust, maintainable production features âą Comfortable collaborating daily in English, written and verbal âą Experience with clean code, unit/integration tests, Git, code reviews, Jira, and CI/CD pipelines âą Experience with Augmented Reality frameworks or computer graphics pipelines preferred âą Familiarity with optimizing models for edge devices and inference runtimes preferred âą Prior experience in retail analytics, planogram compliance, or fine-grained object recognition preferred
âą Full remote or hybrid work from the cozy office in Krakow âą Medical package on preferential terms âą Subsidized English language courses âą Company-provided work equipment âą Opportunities for internal growth and career development âą Access to internal learning resources
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